Temporal Variability of Thermohaline Fine-Structure Associated With the Subtropical Front Off the Southeast Coast of New Zealand in High-Frequency Short-Streamer Multi-Channel Seismic Data
Bibliographic record
Abstract
Seismic oceanography generally makes use of multi-channel seismic reflection data sourced by air gun arrays and long hydrophone streamers to image oceanographic water masses and processes—often piggybacking on surveys that target deeper geological features below the seafloor. However, due to the acquisition methods employed, shallow (upper 200 m or so) regions of the ocean can be poorly imaged with this technique, and resolution is often lower than desirable for imaging fine-structure within the water column. In 2012, we collected a set of higher-resolution seismic lines off the southeast coast of New Zealand, with a generator-injector airgun source and hydrophone streamer configuration designed to improve images of shallower water masses and their boundaries. The seismic lines were acquired with coincident expendable bathythermograph deployments which provides direct ties between physical oceanographic data and seismic data, allowing for definitive identification of the Subtropical Front and associated water masses in the subsurface. Repeat acquisition along the same transect shows significant temporal variability on the scale of hours, illustrating the highly dynamic nature of this important ocean boundary. Comparisons to conventional low-frequency seismic data in the same location show the value of high-resolution acquisition methods in imaging the near-surface of the ocean and allowing subsurface features to be linked to their expressions at the surface.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".