Utilisation de la scanographie pour l'étude des sédiments : influence des paramètres physiques, chimiques et biologiques sur la mesure des intensités tomographiques
Bibliographic record
Abstract
In July 1996, a flash flood resulted in the input of 9 million m3of sediment toward the Bay of Ha! Ha!, leading to the elimination, partly or totally, of the benthic fauna of the bay. In this study, the CT scanner has been used in a highly perturbed environment, the Bay of Ha! Ha!, to both assess (i) the relationships between the variations of tomographic intensities and the sedimentologic parameters of the sedimentary column and (ii) to quantify the biogenic structures resulting from the activity of benthic organisms. Compaction, CaCO3contents, and granulometry of sediments are the most important influences on the variation of tomographic intensities. The scanner allowed the study and quantification, in a non destructive way, of the sediment occupation by biogenic structures and, more particularly, allowed to further assess most of the relative importance of the fine fraction of biogenic structures (0.2501 mm) in the surface sediment layer (05 cm). Sediment occupation by biogenic structures reached maximal values at the upper part of the sedimentary column and decreased with depth. If sedimentary reworking leads to an increase in the sediment porosity, destabilization generated by the activity of organisms is balanced by the consolidation of the wall of the biogenic structures. Bioturbation resulting from the activity of benthic organisms into the sediments has a significant role on the sedimentary structure and biogeochemical processes. It is therefore necessary to quantify the volume of sediments occupied by biogenic structures to assess the activity of benthic organisms in the sedimentary column.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".