Comparison of known spawner abundance from fence counts to visual counts for simplified spawner estimation methods
Bibliographic record
Abstract
Many salmon species are monitored by visual counts of spawners in streams; however, there are few data sets where abundance is known and compared to estimates derived from visual counts. We used spawner fences to obtain known kokanee ( Oncorhynchus nerka) spawner abundance (14 stream-years) on streams that are monitored with annual visual surveys (7 to 9 counts per year) and incorporated similar published data from pink salmon ( Oncorhynchus gorbuscha) (11 stream-years). We investigated the performance of several simplified expansion factor estimation methods with survey life and observer efficiency as unknown nuisance parameters. All visual indices of kokanee and pink salmon spawners from live ground counts were highly correlated to abundance from fence counts ( r2 ≥ 0.96 and 0.89, respectively). Application of cross-validation on out-of-sample data for both species showed that mean% error could range from 13% to 53% on a previously unsampled stream depending on the species, counting method, and visual index used. Predictive performance metrics were less sensitive to counting frequency than observer efficiency and associated variability, which was influenced by the counting method (aerial versus ground surveys).
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".