Patterns of river otter (Lontra canadensis) diet and habitat selection at latrine sites in central British Columbia.
Bibliographic record
Abstract
I investigated patterns in river otter (Lontra canadensis) diet, habitat selection, and behavior at latrine sites in central British Columbia during the ice-free season in 2007 and 2008. I used an Information Theoretic Model Comparison approach to investigate the relationships among otter diet and temporal/spatial parameters and habitat characteristics and the presence, consistency, and intensity of otter activity. Data were collected every two weeks at latrine sites visited by otters. I used a combination of scat content and stable-isotope analysis to investigate the contributions of different prey items to otter diet. Binary and count models were used to predict the presence of individual prey items and number of scats, respectively. A combination of fish spawning period, water body type, and individual lake best described the presence of salmonids, minnows, and insects in otter scat. The relative effects of season and water body varied considerably among the three prey groups found in scats. Scat deposition was positively influenced by a time period when no fish were spawning (early July) and to the kokanee (Oncorhynchus nerka) spawning period (early September). In general, the stable-isotope analysis agreed with the results of the scat content analysis showing a dominance of fish in the diet of otter and a small contribution from other prey sources. The stable-isotope analysis, however, suggested a larger contribution from sockeye salmon and birds relative to data from the scat content analysis. I followed the diet analyses with an investigation of factors that influenced the selection of latrine sites and activity of otter at multiple spatial and behavioural scales. For fine-scale analyses, I performed field measurements at latrine sites and spatially adjacent random sites. At the course landscape scale, I used Geographic Information Systems (GIS) to examine environmental variables that described the broader Tezzeron and Pinchi lake study area. Working at these two spatial scales, I used binary models to p
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".