Bibliographic record
Abstract
White Glacier is located on Axel Heiberg Island in Nunavut, Canada, and has had its mass balance actively monitored since 1960. Due to COVID-19 travel restrictions it not possible for researchers to travel to White Glacier and perform the measurements required. This results in gaps in data required to determine the mass balance for 2018-2020. In this study we aim to collect and process laser altimeter data to be interpolated to calculate an estimate of the Mass Balance of White Glacier. This study will be completed using a geodetic technique that utilizes the Ice Cloud and Elevation-2 (ICESat-2) satellite altimetry data. ICESat-2 is carrying ATLAS which is an Advanced Topographic Laser Altimeter that is equipped with six laser beams divided into three pairs that measure lidar altimetry to derive surface height. The longitude, latitude, datetime, and land ice height values were extracted over the Expedition fjord region using MATLAB. The land ice tracks were brought into ArcGIS for analysis, three repeat tracks in the Expedition Fjord region were selected for analysis to determine the difference in elevation between the premelt seasons of 2019 and 2020 as well as one track comparing the premelt and melt seasons of 2019. These elevation differences will be interpolated as accumulation or ablation dependant on the location on the glacier and used to estimate mass balance.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".