Apparent Survival and Detection Estimates for PIT‐Tagged Slimy Sculpin in Five Small New Brunswick Streams
Bibliographic record
Abstract
Abstract The slimy sculpin Cottus cognatus is an abundant and widespread benthic fish that inhabits cold lakes and rivers in North America. The objective of this study was to estimate survival and detection probabilities for slimy sculpin in relation to several environmental and biological predictors. Passive integrated transponder (PIT) tags were implanted into 337 adult slimy sculpin in five tributaries of the Kennebecasis River, New Brunswick, Canada. A portable PIT tag antenna was used to search for marked individuals from June 2003 to July 2004. Cormack–Jolly–Seber open population models were used to test several predictions and to estimate apparent survival and detection probabilities. We found that survival was high (73–99%) among sampling events; the average period was about 4 weeks (range, <1–22 weeks). Survival was positively related to fish length and negatively related to maximum stream discharge. The mean detection probability of tagged sculpin was 0.80, but it varied among sampling events and with respect to the minimum electrical current of our antenna and the percentage of boulder substrate at the site. This study demonstrates that a portable PIT tag system can be used in conjunction with capture–mark–recapture models to acquire an understanding of the basic life history characteristics of slimy sculpin and possibly other small‐bodied fish in freshwater systems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".