Validating helicopter-based electromagnetic induction (hem) measurements over thick multi-year ice
Bibliographic record
Abstract
The thicknesses obtained from drill-hole measurements on four very thick multi-year ice floes are compared to thicknesses obtained over the same profile areas by a helicopter-based electromagnetic induction (HEM) system. Drill-hole measurements produced average thicknesses of 8.3 m, 6.5 m, 10.2 m and 12.9 m. Compared to individual drill-hole measurements, the HEM overestimated the percentage of ice from 3 to 7 m thick, and did not reproduce thicknesses larger than about 12 m. Since important information about the maximum thickness of the most massive ice features was not captured, it caused the average thickness of very thick multi-year floes to be underestimated by the HEM. The average thickness of the sampling areas on the four examined floes was underestimated by 1.2 to 3.1 m, or from 15 to 24%, with the thickest, most deformed ice floe producing the least favourable agreement. The paper shows that the HEM provides a reasonable estimate of the average thickness of deformed multi-year ice when the ice is less than about 10 m thick, on average however, it should also be noted that 5 of the 24 multi-year floes (20%) on which more than 600 drill-hole measurements have been made over the past three years have had an average thickness of 10 m, or more. Evidence suggests that HEM surveys may be missing a component of Arctic sea ice that is important for offshore operations and seafloor scouring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".