Using multistate occupancy estimation to model habitat use in difficult-to-sample watersheds: bridle shiner in a low-gradient swampy stream
Bibliographic record
Abstract
We used multiseason, multistate patch occupancy models to investigate habitat use of a regionally rare minnow (bridle shiner, Notropis bifrenatus) within a difficult-to-sample, swampy stream system by defining occupancy states as coarse abundance categories (i.e., none, some, many). Habitat patches were repeatedly subsampled during three sampling periods spanning June to August 2011 using a nonstandard purse-and-lift method with a seine net, as poorly defined shorelines, unconsolidated substrate, and emergent vegetation limited beaching and restricted possible sampling locations. Detection probabilities increased from June to August, likely due to increasing catch per effort as age 0 became vulnerable to the gear, supported by the probability of detection being greater when the species was at high abundance, given occupancy. The probability of a habitat patch being occupied increased with the percent of macrophyte cover and decreased with increasing distance from another occupied patch. Decreasing mean depth showed a weak relationship to high abundance, given a patch was occupied. In summary, the multistate occupancy analytical approach was highly informative for developing quantitative habitat relationships and was seen as an effective framework for evaluating habitat use of aquatic organisms that inhabit environments inherently difficult to sample for which imperfect detection and sampling efficiency are of concern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".