HIGH-FREQUENCY SEISMIC REFLECTION SURVEYING FROM AN ICE ISLAND IN THE CANADIAN HIGH ARCTIC
Bibliographic record
Abstract
From 1985 to 1989 high-frequency seismic reflection profiling was\ncarried out from a drifting ice island as part of Project\nI.S.I.S. (Ice island Sampling and Investigation of Sediments),\nrun by the Atlantic Geoscience Centre, Nova Scotia, Canada.\nSeismic sections from this initial study were found to be\nvirtually uninterpretable. To facilitate a greater understanding\nof the problem the analysis has concentrated on the single trace,\nrather than the complete seismic section. In September 1991 a\nseries of experiments were conducted using a digital seismograph\nand sub-ice hydrophone to monitor and record the return signal\nfrom an 8.5 kJ marine Sparker. This has shown that the primary\ninfluence on the initial down-going wavelet and hence the\nresolution attainable, is the complex morphology and acoustic\nnature of the ice itself. Improvements in the signal-to-noise\nratio were gained by digital filtering, stacking and by the use\nof a conical baffle to directionalise the received hydrophone\nsignal from below. A series of recordings made over a two week\ntime window have shown the influence of localised ambient noise\nto be minimal.\nTo complement this study an analysis has been made of the\nacoustic and geotechnical properties of two gravity cores from\nthe study area, in an attempt to understand the acoustic response\nof the ‘first layer’. The work has highlighted the need for\ngreater control over the survey design and has begun to\ninvestigate the viability of high quality digital acquisition for\nfuture surveys in ice covered regions.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".