Melt ponds on sea ice in the Canadian Archipelago: 1. Variability in morphological and radiative properties
Bibliographic record
Abstract
The morphological and radiative properties of melt ponds on first‐year sea ice (FYI) were investigated during the summer of 1997 in the Canadian Archipelago as part of the Collaborative‐Interdisciplinary Cryospheric Experiment (C‐ICE) near Resolute Bay, Nunavut. In this paper we (1) describe a classification technique used to identify surface cover types from airborne videography during the melt pond season, (2) use the classification results to examine the fractional coverage of surface types and morphological characteristics of melt ponds, and (3) provide an estimate of the integrated shortwave albedo of this surface. Cluster analysis on the videography data identified four distinct surface cover types during the summer melt season: snow, saturated snow, light‐colored melt ponds, and dark‐colored melt ponds. Melt pond coverage was found to be highly variable over our study area. We found that pond sizes tended to be twice as large in areas of high melt pond density compared to ponds in areas of low pond density owing to their interconnective nature. Video data also identified an elongated melt pond morphology pattern over the smoothest FYI within our study region. A derived estimate of the integrated shortwave albedo was strongly related to the fractional cover of snow on the surface (R2 = 0.86). An analysis of combined fractional coverage of light and dark melt ponds from two aerial survey dates (Julian Days 181 and 184) revealed an aerial increase in melt ponds of ∼10.3%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".