CO<sub>2</sub>-induced ocean acidification increases anxiety in Rockfish via alteration of GABA<sub>A</sub>receptor functioning
Bibliographic record
Abstract
The average surface pH of the ocean is dropping at a rapid rate due to the dissolution of anthropogenic CO2, raising concerns for marine life. Additionally, some coastal areas periodically experience upwelling of CO2-enriched water with reduced pH. Previous research has demonstrated ocean acidification (OA)-induced changes in behavioural and sensory systems including olfaction, which is due to altered function of neural gamma-aminobutyric acid type A (GABAA) receptors. Here, we used a camera-based tracking software system to examine whether OA-dependent changes in GABAA receptors affect anxiety in juvenile Californian rockfish (Sebastes diploproa). Anxiety was estimated using behavioural tests that measure light/dark preference (scototaxis) and proximity to an object. After one week in OA conditions projected for the next century in the California shore (1125 ± 100 µatm, pH 7.75), anxiety was significantly increased relative to controls (483 ± 40 µatm CO2, pH 8.1). The GABAA-receptor agonist muscimol, but not the antagonist gabazine, caused a significant increase in anxiety consistent with altered Cl(-) flux in OA-exposed fish. OA-exposed fish remained more anxious even after 7 days back in control seawater; however, they resumed their normal behaviour by day 12. These results show that OA could severely alter rockfish behaviour; however, this effect is reversible.
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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 | 0.000 |
| 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 | 0.001 |
| 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".