Micro‐computed tomography: Applications for high‐resolution skeletal density determinations: An example using annually banded crustose coralline algae
Bibliographic record
Abstract
Abstract Warming and acidification of the world's oceans are expected to have widespread consequences for marine biodiversity and ecosystem functioning. However, due to the relatively short record of instrumental observations, one has to rely upon geochemical and physical proxy information stored in biomineralized shells and skeletons of calcareous marine organisms as in situ recorders of past environments. Of particular interest is the response of marine calcifiers to ocean acidification through the examination of structural growth characteristics. Here we demonstrate the application of micro‐computed tomography (micro‐CT) for three‐dimensional visualization and analysis of growth, skeletal density, and calcification in a slow‐growing, annually banded crustose coralline alga Clathromorphum nereostratum (increment width ∼380 µm). X‐ray images and time series of skeletal density were generated at 20 µm resolution and rebinned to 40, 60, 80, and 100 µm for comparison in a sensitivity analysis. Calcification rates were subsequently calculated as the product of density and growth (linear extension). While both skeletal density and calcification rates do not significantly differ at varying spatial resolutions (the latter being strongly influenced by growth rates), clear visualization of micron‐scale growth features and the quantification of structural changes on subannual time scales requires higher scanning resolutions. In the present study, imaging at 20 µm resolution reveals seasonal cycles in density that correspond to summer/winter variations in skeletal structure observed using scanning electron microscopy (SEM). Micro‐CT is a fast, nondestructive, and high‐resolution technique for structural and morphometric analyses of temporally banded paleoclimate archives, particularly those that exhibit slow or compressed growth or micron‐scale structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".