Systematic Data in Biodiversity Studies: Use It or Lose It
Bibliographic record
Abstract
Systematic data in the form of collections data are useful in biodiversity studies in many ways, most importantly because they serve as the only direct evidence of species distributions. However, collecting bias has been demonstrated for most areas of the world and has led some to propose methods that circumvent the need for collections data. New methods that model collections data in combination with abiotic data and predict potential total species distribution are examined using 25,111 records representing 5,123 species of plants and animals from Guyana; some methods use the reduced number of 320 species. These modeled species distributions are evaluated and potential high-priority biodiversity sites are selected based on the concept of irreplaceability, a measure of uniqueness. The major impediments to using collections data are the lack of data that are available in a useful format and the reluctance of most systematists to become involved in biodiversity and conservation research.
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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 | 0.282 | 0.405 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.021 | 0.050 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".