Bibliographic record
Abstract
Upward-looking sonars moored on the sea floor have contributed to our qualitative and quantitative understandings of ocean ice covers by enabling quasi-continuous measurements of ice draft along curvilinear tracks to accuracies as great as 0.05 m. The capabilities of ASL’s own IPS4 instrument to acquire and store such data has been demonstrated in well over 100 deployments in polar and sub-polar ice-infested regions. Data obtained from these deployments has providing ice property and characterization information for platform and operations design, planning, navigation support and for scientific ice and climate studies. Results obtained with recent use of the IPS4 and a sister instrument specialized to shallow water applications have motivated both the development of new deployment methodologies and suggested applications additional to simple ice draft measurements. Particular potential uses such as detecting unconsolidated ice content in lower portions of ice keels as well as the prevalence of loose and/or frazil ice under ice covers and in shallow water areas are discussed. Perceived future needs in both conventional draft profiling and in these and other new applications are used to guide developing requirements for a new generation of IPS instrumentation offering new performance capabilities and additional user-specific configurability. ASL’s vision of this instrumentation and progress toward prototype construction is described.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".