Freshwater and marine indicators of salmon productivity from British Columbia to California and an assessment of risk using climate change projections
Bibliographic record
Abstract
Salmon constitute an important indicator of the ecosystem status for the Integrated Ecosystem Assessment (IEA) of the California Current Large Marine Ecosystem (CCLME). Salmon productivity can be affected by freshwater (FW) and marine (M) environments because of their complex life histories that include spawning, rearing, migration, and maturation in both environments. Furthermore, as climate change will likely manifest in different rates of change among environments, it is important to determine how much FW and M indicators are correlated and to what degree we can tease apart their influences on salmon productivity. In this study, we will focus on Pacific Coho and Chinook salmon from southern British Columbia to California. We will test different approaches with three groupings of data: 1) smolts-per-spawner and smolt-to-adult return rates, 2) age-structured adult recruits-per-spawner, and 3) non-age-structured adult returns. As precision in the type of data declines (moving from groupings 1 to 3), our ability to resolve the influences of FW and M indicators on salmon productivity decreases, but the number of datasets available for analysis increases. A comparison of the trade-offs between data quality and quantity will be valuable. The FW and M indicators we are considering include those at the local/regional scale such as water temperature, flow, number of spawners, upwelling, and dissolved oxygen, and those at the large/atmospheric scale such as Pacific Northwest Index, Pacific Decadal Oscillation index, and Southern Oscillation Index. Correlated indicators will be analyzed with multivariate statistical techniques. In various analyses of salmon productivity, we will test whether FW and M indicators (as original values or part of multivariate indices) affect one of the model parameters and the residuals of the Ricker function. Furthermore, we will assess the relative risk of salmon productivity using climate projections in a 20–50 year time frame. Overall, this work will focus on general patterns of environmental indicators of salmon productivity across the coast, and not primarily on determining predictors for specific populations. Identifying which and to what degree FW and M indicators influence salmon productivity and identifying threshold values will be useful for the CCLME-IEA.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".