Bibliographic record
Abstract
In this paper we closely examine the performance of several propagation models, i.e., KRAKEN (coupled and adiabatic) and PE (energy-conserving), applied to a number of the SWAM'99 range-dependent shallow water test cases (FLAT, DOWN, and UP). We begin by considering range-independent behavior (including the ORCA model) in: the CAL case of Workshop'97 (Vancouver, '97),9 the first segment of FLATa, and the Benchmark Wedge test case3 but with a flat bottom of 200 m depth. We next examine the proper Benchmark Wedge behavior for the sloping bottom for our PE (conserving and nonconserving) and for our normal mode model (KRAKEN, adiabatic and coupled). These preliminary tests confirm that the models are behaving properly under known conditions and that the input parameters have been appropriately set. Thus, when we study the models' behavior on the new SWAM'99 cases we will have some confidence that they are being applied properly. It is nontrivial to run these models even when one is familiar with them. The SWAM'99 test cases which are examined here are run only to 10 km range (five-step segments) and at a single frequency of 25 Hz. No elasticity is considered. We find that all the models generally agree, but there are quantitive differences. Since there are no proper benchmark solutions for these SWAM'99 test cases, it is difficult to determine to what extent any of them are in error. However, for the purposes of Matched Field Processing, particularly the tomographic geoacoustic inversion using adibatic normal modes (KRAKEN), it is likely that the simple adiabatic normal mode KRAKEN model is sufficiently accurate under most circumstances, i.e., unless there is a loss or gain of a critical mode.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".