Designing Sortable Guilds for Multispecies Selective Fish Passage
Bibliographic record
Abstract
ABSTRACT The importance of connectivity for freshwater organisms is widely recognised, yet in‐stream barriers associated with population declines and increased risk of extinction remain globally ubiquitous. Despite their negative consequences, these barriers can protect aquatic communities by limiting the spread of invasive species, leading to conflicting management goals in some regions. Selective fish passage is a solution for the conflicting goals of passing native, desirable species while restricting the spread of invasives. Approaches that can target groups of species sharing similar attributes (i.e. guilds) are likely to be more efficient than those that target species individually, particularly in taxonomically diverse systems. We explored the guild structure of 220 Great Lakes freshwater fishes based on morphological, phenological, physiological and behavioural attributes associated with passage and movement. We identified five distinct guilds as well as the attributes most important for defining these groupings: maximum total length, trophic level, relative eye size, spawning temperature, spawning season, presence/absence of ampullary electroreceptors and the presence/absence of hearing specialisations. The approaches outlined in this work can be generalised to enhance selective fish passage in aquatic ecosystems worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".