Retrospective weight-of-evidence analysis identifies substrate change as the apparent cause of recruitment failure in the upper Columbia River white sturgeon (<i>Acipenser transmontanus</i>)
Bibliographic record
Abstract
A weight-of-evidence evaluation of the potential cause of white sturgeon (Acipenser transmontanus) recruitment failure in the upper Columbia River evaluated 12 recruitment-failure hypotheses based on nine criteria. Recruitment-failure timing was estimated by identifying breakpoints in the hindcasted recruitment time series for three of four spatially distinct groups. Observed spatial and temporal recruitment decline patterns were then compared with expected patterns for each hypothesis (e.g., flow, temperature, turbidity, contaminants, and changes to the riverine fish community). The weight-of-evidence evaluation also considered criteria including coherence with theoretical, factual, and biological evidence and responses to impact reversal. Recruitment failure began about 1968, coincident with the initiation of upstream mainstem flow regulation. An 8- or 9-year lag in the recruitment decline of the Waneta group was particularly informative for hypothesis evaluations. The identification of increased fine substrates at spawning sites as the most plausible explanation for chronic recruitment failure has important implications regarding the apparent life stages affected and potential restoration approaches.
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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 | 0.023 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".