Bibliographic record
Abstract
Pulses of abundance in salmon migrations can arise from single populations arriving at different times, from multiple populations with different timing characteristics, or as a combination of these. Daily observations typically record an aggregate measure of abundance passing some location rather than the abundances of the individual components. An objective method is described that partitions a compound migration into its component parts by exploiting differences in the characteristics of each pulse. Simulated data were used to demonstrate when greater model complexity may be desirable. Three case studies of increasing complexity (Chilko Lake sockeye salmon smolts (Oncorhynchus nerka), large adult Columbia River Chinook salmon (Oncorhynchus tshawytscha), Fraser River salmon test fishery) demonstrate how the model can be applied in practice. Results indicated that Chilko Lake smolts rarely emigrate to sea as a single pulse, that the dates used to distinguish the spring run of Chinook salmon in the Columbia River may be overestimating its abundance, and that pulses of sockeye salmon abundance in a Fraser River ocean test fishery in 2014 may have arisen from some factor other than population composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".