A quantitative analysis of the state of knowledge of turtles of the United States and Canada
Bibliographic record
Abstract
The “information age” ushered in an explosion of knowledge and access to knowledge that continues to revolutionize society. Knowledge about turtles, as measured by number of published papers, has been growing at an exponential rate since the early 1970s, a phenomenon mirrored in all scientific disciplines. Although knowledge about turtles, as measured by number of citations for papers in scientific journals, has been growing rapidly, this taxonomic group remains highly imperiled suggesting that knowledge is not always successfully translated into effective conservation of turtles. We reviewed the body of literature on turtles of the United States and Canada and found that: 1) the number of citations is biased toward large-bodied species, 2) the number of citations is biased toward wide-ranging species, and 3) conservation status has little effect on the accumulation of knowledge for a species, especially after removing the effects of body size or range size. The dispersion of knowledge, measured by Shannon Weiner diversity and evenness indices across species, was identical from 1994 to 2009 suggesting that poorly studied species remained poorly-studied species while well-studied species remained well studied. Several species listed as threatened or endangered under the U.S. Endangered Species Act (e.g., Pseudemys alabamensis , Sternotherus depressus , and Graptemys oculifera ) remain poorly studied with the estimated number of citations for each ranging from only 13-24. The low number of citations for these species could best be explained by their restricted distribution and/or their smaller size. Despite the exponential increase in knowledge of turtles in the United States and Canada, no species of turtle listed under the Endangered Species Act has ever been delisted for reason of recovery. Therefore, increased knowledge does not necessarily contribute appreciably to recovery of threatened turtles.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".