Single-Stream Recycling Inspires Selective Fish Passage Solutions for the Connectivity Conundrum in Aquatic Ecosystems
Bibliographic record
Abstract
Barrier removal is a recognized solution for reversing river fragmentation, but restoring connectivity can have consequences for both desirable and undesirable species, resulting in a connectivity conundrum. Selectively passing desirable taxa while restricting the dispersal of undesirable taxa (selective connectivity) would solve many aspects of the connectivity conundrum. Selective connectivity is a technical challenge of sorting an assortment of things. Multiattribute sorting systems exist in other fields, although none have yet been devised for freely moving organisms within a river. We describe an approach to selective fish passage that integrates ecology and biology with engineering designs modeled after material recycling processes that mirror the stages of fish passage: approach, entry, passage, and fate. A key feature of this concept is the integration of multiple sorting processes each targeting a specific attribute. Leveraging concepts from other sectors to improve river ecosystem function may yield fast, reliable solutions to the connectivity conundrum.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".