Review of tc-2019-190 : Accuracy and Inter-Analyst Agreement of Visually Estimated Sea Ice Concentrations in Canadian Ice Service Ice Charts
Bibliographic record
Abstract
General Comments: This paper presents a good overview of the importance of ice charts for not only navigation guidance, but as a relevant archive for climate monitoring and use as a validation variable.This is a very nice paper that provides some good and novel approaches to evaluate how to provide some level of uncertainty in ice charts, particularly with the Kappa statistic and Krippendorff's alpha applications.Additionally, this is hopefully the start of a trend in producing much needed literature that focuses on the role of ice charts in research and development for environmental monitoring.This includes the challenges with how different data sources should be handled and the variation between information input, including the subjective nature of ice analysts.This topic has always been of great interest to the research community that depend C1 TCD Interactive
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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 | 0.044 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".