Assessment of upstream and downstream passability for eel at dams
Bibliographic record
Abstract
Abstract The American eel (Anguilla rostrata) population has experienced a marked population decline. Habitat loss resulting from dam construction to improve the control and use of freshwater discharge is one of the factors involved. There are some 5600 dams in rivers draining to the St. Lawrence River in Quebec (Canada). Their passability to eels migrating upstream and downstream has been assessed using the Québec Dam Database. Eighteen percent of the dams are used for supplying water and 13% for hydroelectricity, but >50% are used for recreational purposes. Although the majority of the dams are <3 m in height and are made of concrete or earthfill, dams present a great variety of physical characteristics. Passability ranks were assigned to each category of dam based on three assessment criteria: the height of the dam, the materials used in its construction, and its use. Passability to upstream migrants was also assessed from photographs for a subset of dams. The two methods (statistical analysis and the use of photographs) may yield different results, but the two methods were consistent to identify the impassable dams. This analysis shows overall that the problem of passability is more significant for upstream passage than it is for downstream passage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".