Trade-offs between monitoring objectives and monitoring effort when classifying regional conservation status of Pacific salmon (<i>Oncorhynchus</i>spp.) populations
Bibliographic record
Abstract
Conservation objectives aimed at maintaining the diversity of Pacific salmon ( Oncorhynchus spp.) are often expressed as a desire to ensure that spawner abundance is spread out over a number of spawning sites. However, sampling is not usually possible at all sites or in all years. For such incomplete data sets, rotating panel sampling designs and hierarchical estimation models have been suggested as ways to improve monitoring performance. To evaluate the potential benefits of using these approaches to assess the conservation status of coho salmon ( O. kisutch ), we developed a simulation procedure that modelled spatial and temporal variation in salmon abundance at multiple sites within a region. Results show that both approaches were largely unsuccessful at reducing classification errors for conservation status. Furthermore, indicators describing distributions of abundance levels and temporal trends in abundance within a region were more sensitive to missing data than to observation error variance on annual abundance estimates. Thus, sampling effort might be better spent reducing the level of missing data within a regional data set, as opposed to obtaining more precise estimates for only a few site–year combinations. Our results also show that the best monitoring plans for regions depend on monitoring objectives as well as the relative magnitudes of spatial and temporal variability.
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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 | 0.040 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".