ArcticNet 2021 Annual Scientific Meeting Abstracts
Bibliographic record
Abstract
There has been a recent focus on Greenlandic fjord oceanography as half of this island's contribution to sea level rise comes from submarine melting and ice discharge at tidewater glaciers.However, in the Canadian High Arctic, the role of fjords is not well understood, in part because of the lack of oceanic measurements.Two main oceanographic processes occur at the termini of tidewater glaciers during summer.The release of water produced by glacial meltwater runoff that finds its way to the bottom of the glacier is known as subglacial discharge, whereas submarine melting is the direct melting of the glacier by the ocean water.Quantifying subglacial discharge and submarine melting is critical to understanding fjord oceanography and cryospheric change in the catchment.This is typically done by comparing a temperatureconductivity-depth (CTD) profile close to the glacier with one that represents ambient oceanographic conditions farther offshore.Here, we develop a technique to depth-correct the ambient profiles to account for buoyancy difference following mixing with meltwater.Our results show that the standard method for comparing to ambient profiles may underestimate the amount of meltwater by up to 30%.We subsequently use eight years of water profiles taken in Milne Fiord, Nunavut (80.6°N, 82.5°W) to demonstrate the technique and investigate subglacial discharge and submarine melting in this fjord.The results show that both the amount of subglacial discharge and submarine melting meltwater are correlated to the amount of positive degree days (PDD).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.653 | 0.685 |
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".