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Tool: AI Script Generator

Generate field-ready research scripts for arborists

Turn project goals into step-by-step protocols, mobile-ready checklists, drone mission scripts, and reproducible analysis code. Export method text, CSV/GeoJSON schemas, and R/Python snippets that your crew or collaborators can run, test, and version.

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Practical outputs

How this generator helps arborist research

Start with a research question or operational goal and get outputs formatted for field crews, GIS workflows, and reproducible analysis. The generator focuses on: clear objectives and sampling design, discrete mobile fields and validation rules, exportable data schemas (CSV/GeoJSON), and code outlines for R or Python that document cleaning and aggregation steps.

  • Convert methods into mobile checklists with labeled fields and skip logic
  • Produce CSV/GeoJSON-ready schemas for direct import to GIS or databases
  • Create R (tidyverse + sf) and Python (pandas + geopandas) outlines that include data validation and reproducible report sections
  • Embed QA checkpoints: metadata capture, photo naming conventions, and reviewer sign-off prompts

Ready-made prompt starters

Prompt templates for common arborist research tasks

Use or adapt these prompt starters to generate complete, export-ready scripts. Each starter returns: objectives, equipment, step-by-step procedures, QA checks, metadata fields, and suggested export formats.

Field Inventory Protocol

Standardized two-person inventory for urban parks with measurements and photo guidelines.

  • Prompt starter: "Write a step-by-step field protocol for a two-person crew performing a standardized tree inventory in an urban park: objectives, plot layout, measurements (DBH, height estimate, species code), photo guidelines, metadata fields, and QA checks."
  • Output highlights: measurement procedures, example species code column, photo filename convention, recommended metadata entries (observer, device, GPS accuracy).

Disease & Pest Survey Script

Detect and document symptoms, severity scoring, sample handling, and chain-of-custody text.

  • Prompt starter: "Generate a protocol for detecting and recording signs of [disease/pest] across sample plots: symptom checklist, severity scoring rubric, sample collection and lab submission notes, and chain-of-custody text."
  • Output highlights: standardized severity categories, instructions for leaf/tissue sampling, and sample labeling template for lab submission.

Drone Flight & Imagery Capture Script

Flight planning and capture notes optimized for high-resolution canopy imagery.

  • Prompt starter: "Create a drone mission script for canopy imagery collection optimized for 3 cm/pixel resolution: flight height, overlap, ground control points, weather constraints, and post-flight file naming conventions."
  • Output highlights: recommended flight altitude/overlap, GCP placement guidance, and image export naming schema for downstream photogrammetry.

Plot Sampling SOP for Biomass/Allometry

Reproducible stem-sampling and measurement SOP with notes for allometric calculations.

  • Prompt starter: "Draft a reproducible SOP for sampling tree stems for biomass estimates: sampling strata, core samples, measuring protocols, and notes for downstream allometric calculations."
  • Output highlights: sampling strata definitions, core extraction notes, and required metadata for each sampled tree.

Mobile Form Translation

Convert an SOP into discrete fields and validation rules for mobile data collection tools.

  • Prompt starter: "Convert this field protocol into a checklist with discrete field names and validation rules suitable for KoboToolbox or ODK: label names, data types, required fields, and skip logic."
  • Output highlights: field name list, data types (integer/text/choice), validation patterns, and skip-logic examples.

Data Cleaning Pipeline (Python)

Python outline to standardize inventory CSVs and export GeoJSON.

  • Prompt starter: "Provide a Python script outline to clean tree inventory CSVs: parse dates, validate coordinates, deduplicate records, convert species codes to full names, and export GeoJSON for GIS."
  • Output highlights: pandas/geopandas steps, suggested validation checks, and example export snippet.

Data Cleaning Pipeline (R)

R tidyverse + sf workflow to join plot data with LiDAR metrics and render reproducible reports.

  • Prompt starter: "Produce an R (tidyverse + sf) workflow to join plot CSVs with LiDAR-derived canopy metrics, aggregate per-plot summaries, and produce reproducible RMarkdown report sections."
  • Output highlights: join and aggregate code outline, recommended plots, and RMarkdown report placeholders.

Grant Methods / Protocol Text

Peer-ready methods text for grant proposals with sampling design and analysis plan.

  • Prompt starter: "Write a clear, peer-ready methods section describing sampling design, effort, and analysis plan for a grant application focused on urban tree health monitoring."
  • Output highlights: concise sampling rationale, effort description, and suggested language for limits and assumptions.

Interview & Stakeholder Script

Semi-structured interview prompts and consent language for municipal managers.

  • Prompt starter: "Draft semi-structured interview questions and an interviewer script for municipal tree managers about maintenance practices and canopy goals, including consent language."
  • Output highlights: consent script sample and recommended note-taking fields.

Automated Report Template

Template for recurring monitoring reports with placeholders for maps and management recommendations.

  • Prompt starter: "Generate a template for an automated monitoring report: executive summary, methods, data highlights, maps/figures (placeholders), and recommended management actions based on thresholds."
  • Output highlights: section headers, variable placeholders for metrics and maps, and suggested interpretation guidance.

Format-first outputs

Export-ready outputs and code snippets

Each generated script can include: protocol text ready for permit or methods sections, a CSV header schema for direct import, GeoJSON property templates for spatial exports, and short R/Python snippets to run basic cleaning and exports. Below are conceptual examples you can copy and adapt.

  • Example CSV header: observation_id, date_iso, observer, species_code, dbh_cm, height_m_est, lat, lon, photo_filename, notes, gps_accuracy_m
  • Example GeoJSON properties: {"observation_id": "", "species": "", "dbh_cm": 0, "health_status": ""}
  • Python snippet outline: read CSV, validate coords, drop duplicates, map species codes, export to GeoJSON using geopandas
  • R snippet outline: readr::read_csv(), sf::st_as_sf(coords = c('lon','lat')), dplyr::group_by() summaries, rmarkdown report placeholders

Built-in review points

Human-in-the-loop QA & reproducibility

Generated scripts include explicit human-check prompts and version notes to ensure reproducible field work. Use these checkpoints to document decisions, flag data anomalies, and record equipment or environmental constraints that could affect results.

  • Mandatory pre-field checklist: equipment, calibration, observer training notes, and consent/permit numbers
  • Per-sample QA: photo verification, GPS accuracy threshold, duplicate check, and reviewer initial/sign-off field
  • Post-field review: automated flagging rules (missing fields, out-of-range DBH) plus manual reviewer steps and correction logs
  • Version notes: record prompt input, generator version, and manual edits so collaborators can reproduce the final protocol

From script to field

Implementation steps: mobile forms, drone missions, and analysis

Practical guidance for turning a generated script into working tools and deliverables without claiming direct integrations: map field names to your mobile tool, configure drone mission parameters in your flight planning app, and place code snippets into Jupyter or RMarkdown to run reproducible analyses.

  • Kobo/ODK conceptual mapping: convert each protocol field into a labeled question, choose type (text, integer, select_one), add constraints/regex for validation, and implement skip logic for conditional questions
  • QField/QGIS: import CSV/GeoJSON schemas as layers; ensure CRS and coordinate fields match before field collection
  • Drone missions: transfer recommended flight height, overlap, and GCP notes into your flight-planning software; keep naming conventions consistent for imagery
  • Analysis notebooks: paste generated R/Python outlines into a Jupyter or RMarkdown document; add data paths and run validation cells first

FAQ

Can the generated protocol be exported to a mobile survey tool like KoboToolbox or ODK?

Yes — the generator outputs discrete field lists (labels and data types) and suggested validation rules. To implement: map each generated field to a Kobo/ODK question type, add constraints (e.g., numeric ranges, regex) and skip logic, then test the form on a device. The generator provides conceptual field names and skip-logic examples but does not upload forms for you.

How do I ensure a generated script matches regulatory permit language or IRB requirements?

Treat generated text as a draft. Use the provided 'permit/consent' templates and include institution-specific identifiers. Have a human reviewer—project PI, legal officer, or IRB contact—edit wording, confirm sampling permissions, and add required institutional boilerplate before submission.

What checks should I add to a field script to maintain data quality under adverse conditions (rain, low light, GPS drift)?

Include conditional QA steps: require repeat photos if lighting < threshold, record GPS accuracy and flag observations with high drift, add alternate measurement methods (tape/clinometer) if electronic devices fail, and add a post-field review task to reconcile flagged records.

Can the tool create reproducible analysis code for my dataset and what formats are produced?

The generator produces short, reproducible code outlines for R (tidyverse + sf) and Python (pandas + geopandas) that handle common tasks: parsing dates, validating coordinates, deduplicating, mapping species codes, joining LiDAR metrics, and exporting GeoJSON/CSV. These are templates you should adapt with real file paths and then test in Jupyter or RStudio.

How do I adapt a generic script to local species lists, regional codes, or specific measurement units?

Provide the generator with a local species list or code table and your preferred units in the prompt. The generator will incorporate those mappings into field labels and conversion steps. Always validate a small pilot dataset to confirm mapping and unit conversions before large-scale deployment.

What human oversight is recommended when deploying AI-generated protocols in the field?

Recommended oversight includes a PI or senior technician review of methods, on-site training sessions for crews, a pilot test with a small subset of plots, and a documented reviewer sign-off step embedded in the protocol. Keep a change log of edits and decisions.

How do I version and document changes to a generated protocol so collaborators can reproduce results?

Record: prompt text used to generate the script, date and generator version, manual edits with rationale, and a change log in the document header. Include a DOI or repository link for finalized protocols if you publish them.

Can the generator help prepare text for grant proposals or methods sections while avoiding overclaiming?

Yes. The generator creates clear methods text and cautions sections that state assumptions and limitations. Always have a subject-matter expert review the draft to ensure claims are supported by your sampling design and data.

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