TerraDactyl: A tool for connecting environmental data to when and where
Bibliographic record
Abstract
Research ranging from land use planning to ecology benefits from integrating spatial and temporal environmental data. Analyses on multiple environmental datasets are enhanced when there is a common set of variables, improving the ability of researchers to collaborate across a wide variety of projects. Addressing the need, we developed TerraDactyl, an online tool hosted on eDNA Explorer ( ednaexplorer.org ). TerraDactyl intakes user-provided geospatial coordinates and dates to extract environmental values from a series of datasets hosted on the Google Earth Engine (GEE). We demonstrate the utility of TerraDactyl with two case studies . The first study aims to classify protected areas in the US and Canada given only TerraDactyl data. In the second study we reanalyze published community compositional variation California environmental DNA (eDNA) samples to test whether variation is more strongly associated with environmental factor groups such as soil and topography when more variables are added by TerraDactyl. While some current limitations remain, such as the gaps in data available in polar and coastal regions, TerraDactyl offers a robust integrative tool to assist biodiversity and environmental research that has potential for expansion to include more datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".