Chutes and ladders and other games we play with rivers. I. Simulated effects of upstream passage on white sturgeon
Bibliographic record
Abstract
River fragmentation by dams has often preceded declines in sturgeon populations, which suggests that reconnecting populations would contribute to their recovery. This study used a population viability model to quantify the effects of upstream passage at dams on white sturgeon (Acipenser transmontanus) metapopulations inhabiting a series of long (source) and short (sink) river segments. Simulated river systems with high interspersion of long and short segments and a long segment far upstream supported the healthiest metapopulations, suggesting biological criteria for siting dams. Contrary to expectation, the model predicted that upstream passage, without screening or downstream passage, was harmful to the metapopulation as a whole. However, upstream passage was beneficial when downstream migration was either restricted (by screening larger fish) or made safe (by providing downstream passage). The combination of upstream and downstream passage was most beneficial at dams above a long river segment. Screening, alone or combined with upstream passage, was most beneficial at dams below a long segment. These theoretical results highlight the need for field research to evaluate management options and provide practical guidance for restoring sturgeon metapopulations in large rivers.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".