Use of recent and historical records to estimate status and trends of a rare and imperiled stream fish, <i>Percina jenkinsi</i> (Percidae)
Bibliographic record
Abstract
Rarely encountered animals may be present but undetected, potentially leading to incorrect assumptions about the persistence of a local population or the conservation priority of a particular area. The federally endangered and narrowly endemic Conasauga logperch ( Percina jenkinsi ) is a good example of a rarely encountered fish species of conservation concern, for which basic population statistics are lacking. We evaluated the occurrence frequency for this species using surveys conducted with a repeat-observation sampling approach during the summer of 2008. We also analyzed museum records since the late 1980s to evaluate the trends in detected status through time. The results of these analyses provided support for a declining trend in this species over a portion of its historical range, despite low estimated detection probability. We used the results to identify the expected information return for a given level of monitoring where the sampling approach incorporates incomplete detection. The method applied here may be of value where historic occurrence records are available, provided that the assumption of constant capture efficiency is reasonable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".