Multi-technique surface geophysical surveys over Devon Ice Cap, Canadian Arctic
Bibliographic record
Abstract
This dataset was acquired during a multi-technique surface geophysical campaign in May 2022 over Devon Ice Cap, Canadian Arctic. Description of data: Seismic 9 km of active source seismic reflection data raw segy files for line A and line B seismic observation log matlab script for plotting a raw stack of line A and line B coordinates of each seismic spread, with the ice surface elevation and estimated bed elevation Transient electromagnetic (TEM) 7 large loop TEM soundings with 500 x 500 m loop with receiver 250 m outside the loop (away from the transmitter) USF files for each sounding coordinates for each TEM sounding TEM observation log Magnetotelluric (MT) 17 MT stations raw, unprocessed EDI files coordinates for each MT station MT observation log Time series data (~80GB) can be found at: https://drive.google.com/drive/folders/1OyCIP_B3VUJ4-ULSp8YOAPuNEMHuNcN-?usp=share_link Acknowledgments We thanks the Polar Continental Shelf Program for logistical support throughout the field season; Rob Harris at Geonics for his support and help with the TEM method; Zoe Vestrum at the University of Alberta for her MT support during deployment to the field; Funding This work was funded by the Weston Family Foundation. The aircraft hours were funded by the Polar Continental Survey Program (PCSP) and ArcticNet. MT survey was supported by a NSERC Discovery Grant to Martyn Unsworth and the Future Energy Systems program at the University of Alberta. Corresponding Author Siobhan Killingbeck skillin1@ualberta.ca
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".