Bibliographic record
Abstract
The sediment rejection ability of 8 coral species of 5 families and 3 morphologies were assessed in a series of short term exposure tests over a sedimentation range of 0.5–40 mg cm − 2 d − 1 and one longer term exposure test of 235 mg cm − 2 . Sediment accumulation rates on live corals and dead (enamel-covered) skeletons varied between morphologies, with branching species often more adept at self-cleaning. Flow rates (0–17 cm s − 1 ) significantly affected sediment-shedding ability as did differences in particle sizes, with coarse silt rejected faster than fine silt, but only at very high (235 mg cm − 2 ) deposition rates. Siliciclastic sediment was rejected faster than carbonate sediments and smothering for many days by mms of low organic content carbonate sediment resulted in bleaching, but no mortality. The findings are discussed with respect to turbidity generated in natural and dredging-related resuspension events and in the context for impact prediction for dredging projects. • Branching corals are highly adept at cleaning their surfaces of depositing sediments compared to other morphologies. • Flow rates significantly affected corals' sediment-shedding ability. • Siliciclastic sediment was rejected faster by corals than carbonate sediment. • Coarse silt was rejected faster than fine silt, but only at very high sediment deposition rates. • Smothering of corals for many days by mms of low organic content carbonate sediment resulted in bleaching, but no mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".