Spatial distribution of <i>Alitta virens</i> burrows in intertidal sediments studied by axial tomodensitometry
Bibliographic record
Abstract
Relationships between sediment characteristics and the spatial organization of biogenic structures remain poorly understood, albeit important for characterizing bioturbation impacts and small-scale ecological patterns. Using axial tomodensitometry (CT-scan) and core sectioning, we studied the spatial distribution of Alitta virens burrows in sediment cores from two mudflats with different degrees of exposure along the St Lawrence Estuary, Canada. A variety of burrow morphologies was observed at both sites, with most being I-shaped. Most values measured (organic matter content, mean tomographic intensity, the number and diameter of burrow shafts and the percentage of space they occupy per transverse section) covaried with depth. The more sheltered site had higher organic matter and mud contents, and lower average tomographic intensity values. The spatial distribution of burrow shafts also differed between sites, with the more sheltered site having a higher number of burrow shafts and percentage of biogenic space in the upper sediment column, as well as a greater volume of biogenic structures that were connected to the surface (although intra-site variability was great). Along the horizontal plane and at various depths within sediments, burrow shafts were more randomly distributed at the exposed site, compared to a more even, maximal spacing at the sheltered site. In addition to finding differences in burrow organization at two spatial scales, we found significant correlations between tomographic intensity and (1) the number of burrow shafts, (2) biogenic space and (3) organic matter content. CT-scan data, including tomographic intensity, are useful for examining and comparing biogenic structures in sediment cores.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".