Testing the hypoxia tolerance and hypoxic performance of fishes: A two-tier screening approach
Bibliographic record
Abstract
With regional hypoxic episodes in the world’s oceans becoming more severe and more frequent, a valuable management need will be a screening protocol to characterize the full diversity of hypoxic robustness among fishes (defined here as their tolerance and performance). Yet, high-throughput screening protocols to deal with the almost 33,000 fish species have the potential to trade off convenience with the information depth normally provided with comprehensive but more time-consuming phenotyping protocols. As a solution, we propose a two-tier testing approach, one that combines a simple, rapid, cost-effective, and well-established hypoxia challenge test (a measure of hypoxic tolerance) with an individual-based hypoxic performance test that defines maximum aerobic capacity over a wide range of ambient water oxygen levels (a hypoxic performance curve). The later test would be performed on selected indicator species identified from the tolerance screening test. Here we describe the two methodological approaches for this two-tier approach using some existing data to illustrate the potential for their applications in marine science.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".