Increased food supply mitigates ocean acidification effects on calcification but exacerbates effects on growth
Bibliographic record
Abstract
Abstract Because many of the negative effects of ocean acidification on marine life may result from underlying energetic short-falls associated with increased metabolic demands, several studies have hypothesized that negative responses to high CO2 could be reduced by energy input. Although this hypothesis was supported by a recent meta-analysis, we believe that the meta-analytic calculation used was not appropriate to test the stated hypothesis. Here, we first clarify the hypothesis put forward, the crux being that the effects of increased food supply and CO2 interact statistically. We then test this hypothesis by examining the available data in a more appropriate analytical framework. Using factorial meta-analysis, we confirm that food addition has a positive effect and CO2 has a negative effect on both growth and calcification. For calcification, food addition did indeed reduce CO2 impacts. Surprisingly, however, we found that food addition actually exacerbated the effects of acidification on growth, perhaps due to increased scope upon which CO2 effects can act in food-replete situations. These interactive effects were undetectable using a multilevel meta-analytic approach. Ongoing changes in food supply and carbonate chemistry, coupled with under-described, poorly understood, and potentially surprising interactive outcomes for these two variables, suggest that the role of food should remain a priority in ocean acidification research. Arising from: L. Ramajo et al., Sci. Rep. 6: 19374 (2016).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".