California steelhead populations were regionally buffered and individually resistant to a severe multi-year drought
Bibliographic record
Abstract
Weather extremes, such as drought, are predicted to be a strong determinant of species persistence under climate change. Yet predictions often fail to consider that variation in streamflow responses, variation in population dynamics, or adaptations to drought could buffer species against extremes. In this study, we examined the responses of eight California (USA) steelhead populations to a severe drought from 2012 to 2016. We observed that streamflows were highly synchronous across the region in all seasons and did not appear to buffer drought impacts. Population dynamics were variable across the region and did appear to buffer the region from drought impacts. Some populations had very low productivity for 4 years associated with the drought, while others had slightly below-average productivity for only 2 years. Population synchrony was associated with spring-smolt flow, temperature, and drought over time, but was not associated with winter-spawner or summer-juvenile drought, suggesting populations may be adapted to drought conditions. Our results highlight how regional buffering and adaptation can be important mechanisms against climate extremes both now and into the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".