Monitoring lake populations of Eastern Sand Darter (<i>Ammocrypta pellucida</i>): a comparison of two seines
Bibliographic record
Abstract
For many imperiled fishes in the Laurentian Great Lakes basin, detection protocols and population monitoring programs are lacking. In this study, we used a repeat-sampling approach to compare the effectiveness of two seines (bag and beach) to detect and characterize the abundance of lake-dwelling populations of Eastern Sand Darter (Ammocrypta pellucida); a threatened species in Canada. Compared to the bag seine, the larger beach seine collected a greater number of Eastern Sand Darter and detected the species at more sampling sites. Model-averaged estimates of detection probability were also greater for the beach seine (p = 0.72) than bag seine (p = 0.48). A decline in catch over repeated seine hauls occurred at less than a third of the sample units. Mean capture probabilities were 0.41 in units sampled by beach seine, and 0.37 in units sampled by bag seine, when depletion occurred. Sizes of Eastern Sand Darter collected by each seine were significantly different, with fewer small (<40 mm total length) individuals found in bag seine hauls. Power analyses indicate that data collected with either seine are expected to detect changes in local distribution of 50% or greater. Power to detect future changes in Eastern Sand Darter abundance of 50% or greater is predicted to be higher for the beach seine, and to increase with the number of seine hauls at a site. Catch differences between seines are interpreted to reflect the greater area sampled by the larger beach seine.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".