Bibliographic record
Abstract
The long-term goal of this work is to develop a set of tools that can be used to determine insitu values for certain sediment geotechnical properties that are needed to predict the potential for burial when various objects such as mines impact the sea bottom. OBJECTIVES The principal objective of this work has been to develop a set of mechanical, remote-sensing tools that will permit the determination of the main geotechnical variables that control mine burial in the bottom. These variables include the shear strength of both soft, fine-grained sediments and coarser, dilative granular sediments. A further objective is to understand better the response of typical near-shore deposits composed of layered fine and coarse sediment. APPROACH Over the past several years our field work has led to the development of a number of remote-sensing tools that have direct application to the problem of mine burial prediction. As an example, the penetration resistance measured by several different types of probe that we have developed is directly related to the bearing capacity of the sediment which is of prime importance in studies of mine burial in the seafloor. These probes include XBP, an expendable bottom penetrometer, PROBOS, a modified version of the Canadian STING penetrometer and a quasistatic penetrometer, STATPEN, that measures both cone and sleeve penetration resistance to a depth of 2 meters into the sea floor. The XBP probes have been used to map critical areas in recent NATO exercises aimed at “Rapid
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.057 |
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".