A quantitative analysis of the state of knowledge of turtles of the United States and Canada
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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