The importance of open data describing prey item species lists for endangered species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 it