Synthesis of Thresholds of Ocean Acidification Impacts on Decapods
Bibliographic record
Abstract
Assessing decapod sensitivity to regional-scale ocean acidification (OA) conditions is limited because of a fragmented understanding of the thresholds at which they exhibit biological response. To address this need, we undertook a three-step data synthesis: first, we compiled a dataset composed of 27,000 datapoints from 55 studies of decapod responses to OA. Second, we used statistical threshold analyses to identify OA thresholds using pH as a proxy for 13 response pathways from physiology to behavior, growth, development and survival. Third, we worked with the panel of experts to review these thresholds, considering the contributing datasets based on quality of the study, and assign a final thresholds and associated confidence scores based on quality and consistency of findings among studies. The duration-dependent thresholds were within a pH range from 7.40 to 7.80, ranging from behavioral and physiological responses to mortality, with many of the thresholds being assigned medium-to-high confidence. Organism sensitivity increased with the duration of exposure but was not linked to a specific life-stage. The thresholds that emerge from our analyses provide the foundation for consistent interpretation of OA monitoring data or numerical ocean model simulations to support climate change marine vulnerability assessments and evaluation of ocean management strategies.
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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 | 0.024 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".