Peeling the Otolith of Fish: Optimal Parameterization for Micro-CT Scanning
Bibliographic record
Abstract
In this paper, we aim to provide optimal parameters for micro-computed tomography scans of fish otoliths. We tested fifteen different combinations to sagittae. The images were scaled to Hounsfield units, and segmented in two distinct volumes-of-interest (external and internal). The strategy we applied, for identifying optimum scan settings for otoliths, included analyses of the sinogram, the distribution of the Hounsfield units and the signal-to-noise ratio. Based on these tests, the optimum sets of parameters for the acquisition of tomographic images of sagittal otoilths were 80 kV, 220 microA and 0.5 mm aluminum filter.The method allowed 3D shape analysis, internal and external density distribution, layer-by-layer density segmentation, and a potential objective method to count growth rings in otoliths. It was possible to compare mean densities between species, and we observed a significant difference among them. In addition, there are ontogenic changes, which could be increasing or decreasing the density. In this study, we applied tomography for several otolith analysis, that could be of great interest for future studies in diverse areas that use otoliths as the basic structure of analysis, or represents a new research line called eco-densitometry of otoliths, where tomography could be applied to explore the density within an ecological perspective.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".