Patterns of magnesium content in Arctic bryozoan skeletons along a depth gradient
Bibliographic record
Abstract
A growing body of evidence suggests that ocean acidification acting synergistically with ocean warming alters carbonate biomineralization in a variety of marine biota. Magnesium often substitutes for Ca in the calcite skeletons of marine invertebrates, increasing their solubility. The spatio-environmental distribution of Mg in marine invertebrates has seldom been studied, despite its importance for assessing vulnerabilities to ocean acidification. Because pH decreases with water depth, it is predicted that levels of Mg in calcite skeletons should also decrease to counteract dissolution. Such a pattern has been suggested by evidence from echinoderms. Data on magnesium content and depth in Arctic bryozoans (52 species, 103 individuals, 150 samples) are here used to test this prediction, aided by comparison with six conceptual models explaining all possible scenarios. Analyses were based on a uniform dataset spanning more than 200 m of coastal water depth. No significant relationship was found between depth and Mg content; indeed, the highest Mg content among the analyzed taxa (8.7 % mol MgCO 3 ) was recorded from the deepest settings (>200 m). Our findings contrast with previously published results from echinoderms in which Mg was found to decrease with depth. The bryozoan results suggest that ocean acidification may have less impact on the studied bryozoans than is generally assumed. In the broad context, our study exemplifies quantitative testing of spatial patterns of skeletal geochemistry for predicting the biological effects of environmental change in the oceans.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".