Session B4: How Fast do Fish Swim? A Global Assessment of What We Know and What We Don't Know
Bibliographic record
Abstract
Abstract:\nCentral to the question of river connectivity is the question of fish swimming performance. As fish passage challenges move away from individual species concerns to more holistic ecosystem considerations the demand for swimming performance data across a wide range of species is growing. In order to establish a baseline for what is known, we have compiled a species list for which swimming performance is known using three leading databases: the Joint AFS-EWRI Fish Passage Reference Database, the USFS Fish Passage Resource Library, and the Department of Fisheries and Oceans Canada’s Icthyomechanics Database. Between these three databases the swimming performance of 233 individual species have been catalogued. The most recent global fish species count from FishBase and the Catalogue of Fishes indicate that there are 32,900 known species of fish. We therefore are certain that we know some information about the swimming capability of 0.7% of the world’s fish species. In order to establish what is unknown, a semi-automated journal search of each of the 33,000 species is underway. Full results of this assessment will be presented at the conference. Conservative early estimates are that some swimming performance data exists for at most 10% of the world’s fish species. There is a wide range between what we know with some confidence (0.7%) and what we’re confident that we don’t know (10%), indicating a need for the expansion of existing databases. This investigation focused solely at the species level. Similar investigations at the genus and family levels will likely provide estimates of abilities where species level data are incomplete. A regional study for fishes in the State of Ohio, U.S., indicates significant improvement in knowledge at coarser scales. Further resources dedicated to the development and maintenance of these databases would foster the growth of the global fish passage community.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.026 |
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".