The importance of open data describing prey item species lists for endangered species
Bibliographic record
Abstract
Abstract Open data and code can be transformative tools in supporting evidence‐informed solutions for stakeholders. Data can take many forms of evidence in the discipline of applied ecology including tables, lists, maps and visualizations to name a few. Endangered and listed species are often a catalyst for research, conservation and planning. Here, a novel, open data set summarizing all the reported diet and prey items for all endangered, terrestrial dryland species listed in central California is provided as a case study. These data highlight the critical need for sharing data rapidly and transparently to support ecological solution science. Systematic review practices were used, data were compiled and the resulting data set was published in an open access, federated data repository using ecological metadata language and FAIR principles. The goal is to show that these data can now be used and analysed by applied ecologists and stakeholders to identify not only the habitat and spatial needs for the endangered species but to widen the conservation protection net to include prey species. Conserving viable habitat with higher likelihoods of prey presence will better support conservation of endangered species, and data describing reported species are a crucial first step. Interactive tables, local species lists and maps are simple tools that can now be developed regionally with open data such as these.
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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 | 0.126 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".