How do the organic and mineral fractions drive the opacity of fish otoliths? Insights using Raman microspectrometry
Bibliographic record
Abstract
We investigated the relationships between the opacity and the physico-chemical characteristics of fish otoliths and more specifically their aragonite and organic fractions. The analysis of these two fractions on otolith macrostructures was performed using Raman microspectrometry on both translucent and opaque zones of otoliths of pollock (Pollachius virens) and European hake (Merluccius merluccius). The magnitude of the Raman signatures of the aragonite and organic fractions were strongly correlated to otolith opacity with maxima in translucent zones. Opacity models, built from Raman signatures, successfully predicted the observed opacity for both species. A partial decorrelation of different aragonite signatures between translucent and opaque zones was revealed and discussed in terms of organisation (size, orientation) of aragonite crystals. Two categories of organic signatures with opposite effects on the opacity were identified, suggesting differences in organic compounds and (or) variations in their relative quantities. These original contributions provided new insight for understanding otolith biomineralization mechanisms as well as for interpreting and discriminating otolith macrostructures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".