Session A4 - On the Cutting-Edge: Optimizing Fish Passage Mitigation Decisions in California Watersheds
Bibliographic record
Abstract
The California Fish Passage Forum is a consortium of state and federal agencies and NGOs whose mandate is to improve fish passage in anadromous waters. The Forum is now embarking on the implementation of a state wide methodology for prioritizing the removal of artificial fish passage barriers. The methodology, which employs highly sophisticated optimization modeling and solution techniques, represents a radical improvement over standard, scoring-and-ranking type procedures commonly used for prioritizing barriers in the US, Canada and other parts of the world. Optimization based methods provide a systematic and objective means of targeting barrier mitigation decisions which maximize restoration gains given available resources. The optimization methodology being implemented by the Forum integrates information on barrier location, passability and cost together with river habitat and quality data for multiple target species in order to identify cost-efficient passage improvement strategies. Critically, the spatial structure of barriers and the interactive effects of passage improvement on longitudinal connectivity are explicitly taken into consideration. Another key feature of the Forum's prioritization methodology is its ease of use. A user-friendly Windows based program, replete with a graphical user interface, has been implemented, allowing Forum members to quickly and easily generate optimized solutions as well as perform basic what-if analyses in terms of running different budget scenarios and or varying the relative weightings placed on individual target species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.004 |
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".