A Field Method for Determining the Firmness of Colonized Sediment Substrates
Bibliographic record
Abstract
Abstract Substrate firmness influences the erodibility, remobilization, and topographic expression of that substrate. Sediment distribution patterns, remobilization of sediment, and the architecture of biogenic sedimentary structures are strongly affected by the firmness and cohesiveness of the sediment. Given the potentially important role sediment firmness plays in different depositional settings it is important to have a consistent means of evaluating it. This paper demonstrates that a modified metallurgical technique, the Brinell hardness test, can be used to produce accurate and consistent firmness data in modern depositional settings. In this method a glass or metal sphere (the indentor) is dropped from a fixed height into a cohesive medium; the size of the indent produced is inversely proportional to the firmness of the media. Firmness values can be reported as a pressure exerted by the substrate (kPa). This method has some advantages over standard penetrometers, such as: ease of use, portability and simplicity of equipment, testing a large area, and flexibility of calculation. Field tests show that this method is accurate if the indentation diameter is between 10% and 80% of the indentor diameter. The method is inappropriate for dry, unconsolidated sand and thixotropic mud. It is, however, extremely useful for assessing the firmness of a wide range of soft to firmground sediments that are composed of clay through coarse sand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".