Two‐current choice flumes for testing avoidance and preference in aquatic animals
Bibliographic record
Abstract
Summary Aquatic chemical ecology is an important and growing field of research that involves understanding how organisms perceive and respond to chemical cues in their environment. Research assessing the preference or avoidance of a water source containing specific chemical cues has increased in popularity in recent years, and a variety of methods have been described in the scientific literature. Two‐current choice flumes have seen the greatest increase in popularity, perhaps because of their potential to address the broadest range of research questions. Here, we review the literature on two‐current choice flumes and show that there is a clear absence of standardized methodologies that make comparisons across studies difficult. Some of the main issues include turbulent flows that cause mixing of cues, inappropriate size of choice arenas for the animals, short experiments with stressed animals, failure to report how experiment and researcher biases were eliminated, general underreporting of methodological details, underutilization of collected data and inappropriate data analyses. In this review, we present best practice guidelines on how to build, test and use two‐current choice flumes to measure the behavioural responses of aquatic animals to chemical cues, and provide blueprints for flume construction. The guidelines include steps that can be taken to avoid problems commonly encountered when using two‐current choice flumes and analysing the resulting data. This review provides a set of standards that should be followed to ensure data quality, transparency and replicability in future studies in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".