Ideal gas model adequately describes movement and school formation in a pelagic freshwater fish
Bibliographic record
Abstract
Movement is a fundamental aspect of the population and community ecology of many organisms, yet, until recently, it has been difficult to measure in the wild. Consequently, simple assumptions are often used to represent movement; a key assumption found in many classic theoretical ecological models (e.g., predator–prey interactions) is that organisms move like ideal gas particles. Here, we test whether this assumption adequately describes the movement of the Cisco (Coregonus artedi) and its schools using fisheries acoustic surveys and mathematical models. We find that several of the individual components of an ideal gas model (IGM) have some inconsistencies with Cisco behavior, yet overall patterns of school formation are close to IGM expectations. For both individual fish and schools: 1) the spatial distributions were random or slightly clumped; 2) the swimming speed distributions were unimodal but significantly different from normal; 3) horizontal movement was more frequent than depth changes; and 4) movement trajectories across the acoustic beam sometimes deviated from straight lines. However, including the average individual and school swimming speeds and known nighttime densities in an IGM generated values that were similar to the observed values for: 1) the time required for schools to form in the morning and 2) school encounter rates.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".