THE IPOS FRAMEWORK: LINKING FISH SWIMMING PERFORMANCE IN ALTERED FLOWS FROM LABORATORY EXPERIMENTS TO RIVERS
Bibliographic record
Abstract
ABSTRACT The current understanding of the effects of turbulence on the swimming performance of fish is primarily derived from laboratory experiments under pressurised flow swim tunnels and open‐channel flow facilities. These studies have produced valuable information on the swimming mechanics and behaviour of fish in turbulent flow. However, laboratory studies have limited representation of the flows fish experience in nature. The flow structure in rivers is imparted primarily by the highly heterogeneous nonuniform bed, and the flow is generally much more complex than in laboratory experiments. The goal of the current work is to direct future laboratory and field studies to adopt a common framework that will shape the integration of both approaches. This article outlines four characteristics of turbulent flow, which we suggest should be evaluated when generalising results from fish turbulent studies in both the laboratory and the field. The framework is based on four turbulence characteristics that are summarised under the acronym IPOS: intensity, periodicity, orientation and scale. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".