Collaborative bedrock mapping of White Glacier basin, Axel Heiberg Island, Nunavut
Bibliographic record
Abstract
This article outlines a collaborative project in development involving participants in the GEM 2 HALIP Activity and the Laboratory for Cryospheric Research, University of Ottawa. Glaciological research in the vicinity of White Glacier, Expedition Fiord, has recently involved the combined use of Structure from Motion photogrammetry methods and ultra-high resolution GigaPan© images to study landscape evolution. Here we propose to apply these techniques to generate a detailed geological map of the area centred on the Between Lake massive sulphide showing. The requirements include (a) sufficient camera resolution, (b) the availability of high-resolution satellite imagery, and (c) ground-based measurements using differential GPS systems. A 4-step approach is proposed that involves limited helicopter survey work to establish ground-control markers; the acquisition of high-resolution, spectrally-rich satellite images such as SPOT6 or WorldView3; the use of panoramic photography using GigaPan©; and targeted sampling of ridges and nunataks to ground truth a preliminary remote predictive geological map. The requirements as well as the mutual benefits to be gained in glaciological and geological research are discussed. For example, improved bedrock mapping could help to better delineate the extent of the massive sulphide deposit, and improve understanding of controls on the subglacial hydrology and basal motion of White Glacier.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".