Environmental DNA (eDNA) detection and habitat occupancy of threatened spotted gar (Lepisosteus oculatus)
Bibliographic record
Abstract
Abstract Determining the occurrence and site occupancy of rare and endangered species can be challenging, particularly without causing harm or stress to the species of concern. Environmental DNA (eDNA) detection was used to assess habitat occupancy by spotted gar (Lepisosteus oculatus), which is federally listed as Threatened in Canada, with known occurrences limited to a small number of locations in southern Ontario. Quantitative polymerase chain reaction (qPCR) assays were developed to detect spotted gar eDNA, which was detected in all but one previously recorded location. The eDNA method was shown to be more effective than traditional netting for detecting spotted gar habitat use. The use of qPCR allowed for quantification of substantial variation in detection strength (copy number) among replicate eDNA samples, with implications for establishing sampling designs for detection and surveillance. The use of eDNA for detection and monitoring of aquatic species of conservation concern shows great potential as a non‐invasive method for assessing species occurrences and habitat occupancy, as well as for informing targeted sampling by conventional capture methods. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".