Evaluating Pacific cod migratory behavior and site fidelity in a fjord environment using acoustic telemetry
Bibliographic record
Abstract
Pacific cod (Gadus macrocephalus) inhabiting Prince William Sound (PWS) may constitute a localized population separate from Gulf of Alaska (GOA) populations; however, connectivity between these regions has not been previously explored. To address this knowledge gap, we investigated Pacific cod migratory behavior and site fidelity using passive acoustic telemetry techniques. Acoustic-tagged Pacific cod (n = 111) were monitored by Ocean Tracking Network acoustic arrays located at the straits and passages connecting PWS with the GOA and arrays deployed in two PWS fjords. Few Pacific cod tagged in PWS moved to the PWS–GOA boundary (1.8%), indicating that demographic connectivity with the GOA was low. Furthermore, 77% of tagged cod spent at least 90% of the time they were known to be alive within small (less than 30 km2) fjords. Cod were present at monitored fjords every month of the study, though some cod migrated away from the fjords during the summer and returned the following winter (11% in 2015 and 5% in 2016). Using continuous-time multistate Markov models, we determined that movement behavior was related to fish length. Larger fish tended to emigrate from monitored fjords more often and undergo longer duration migrations.
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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 | 0.001 | 0.003 |
| 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".