Where do sea lions live? Interspecific interactions and abiotic factors predict Steller sea lion habitat.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Habitat selection by species is dependent on both abiotic factors and species interaction. With regards to species interaction, competition and facilitation can play a critical role regarding how a species selects its habitat. Previous work has suggested that Steller sea lions (Eumetopias jubatus) have been displaced from their haulout sites due to competition with California sea lions (Zalophus californianus). The purpose of our study is to understand what factors determine the number of Steller sea lion present at a haul out site in the Barkley Sound area in Bamfield, BC. We tested this by asking if the number of Steller sea lions at a haulout site at a certain time is related to the presence of California sea lions (as a proxy for interspecific interaction), time of day, and tide height or a combination of two or three of these variables. After running a generalized mixed effect model and competing our models using Akaike Information Criteria, our results indicated that tide height was the best predictor for explaining the number of Steller sea lions present at a haulout site. However, our results also indicated that the presence of California sea lions and time of day may play a role in determining Steller sea lion haulout sites as well. We found from this study that both species interaction and abiotic factors need to be collectively considered when predicting the mechanisms underlying species habitat choice in marine ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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 it