Comment on tc-2022-181
Bibliographic record
Abstract
Melting and refreezing processes in the firn of Devon Ice Cap control meltwater infiltration and runoff across the ice cap, but their full spatial extent and effect on near-surface structure is difficult to measure with ground-based traverses or existing satellite remote sensing. Here, we derive the coherent component of the near-surface return from airborne ice-penetrating radar over Devon Ice Cap, Canadian Arctic, to characterize firn containing centimeter to meter-thick ice layers (i.e., ice slabs) formed from refrozen meltwater in firn. Comparison with reflectivities using a thin layer reflectivity model, informed by ground-based radar and firn core measurements, indicate that the coherent component is sensitive to the near-surface firn structure composed of quasi-specular ice and firn layers, limited by the bandwidth-constrained radar range resolution. By leveraging their differences in range resolution, we assess the use of dual-frequency airborne ice-penetrating radar to characterize the spatial and vertical near-surface structure of Devon Ice Cap. Our results suggest that average ice slab thickness throughout the Devon Ice Cap percolation zone ranges from 4.2 to 5.6 m. This implies conditions that can enable lateral meltwater runoff and potentially contribute to the total surface runoff routed through supraglacial rivers down glacier. Together with the incoherent component of the surface return previously studied, our dual-frequency approach provides an alternative method for characterizing bulk firn properties, particularly where ground-based and higher frequency radar data are not available.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.031 | 0.017 |
| Insufficient payload (model declined to judge) | 0.050 | 0.043 |
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".