Making the most of existing data in conservation research
Bibliographic record
Abstract
Much attention in recent years has been focused on making biodiversity data open and accessible to researchers. Yet ensuring the availability of these data is only the first step in preventing data waste. Here, we argue that researchers need to do a better job of using available datasets. We recommend that researchers search for existing data sources to serve their needs first, that they work to integrate multiple data sources when one alone will not suffice, and that they aim to explore research topics that will directly inform conservation action. We provide a roadmap with resources and examples to help guide conservation researchers towards better data-use practices. The vast quantities of biodiversity data, coupled with advanced techniques for using and integrating datasets, will play a key role in determining how to halt biodiversity declines. Making data open and accessible is only the start; we must be sure that we are using that existing data to conduct further research and inform decisions.
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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 | 0.296 | 0.542 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.032 | 0.035 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.041 | 0.113 |
| Open science | 0.015 | 0.041 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".