Comment on tc-2022-241
Bibliographic record
Abstract
Abstract. This paper presents the analysis results of temperatures collected at three monitoring stations which were used to study ice freezing and thawing processes on a reservoir along Irtysh River. The measured temperatures were comprehensively analyzed and correlated with air temperature measured at a meteorological station. The results showed that air temperature was closely related to temperature at the ice surface, e.g., T40, T20 and T0, and temperatures in ice almost increased linearly with depth. In addition, ice thicknesses were calculated based on measured temperatures along arrays of temperature and compared with that calculated using a simplified Stefan’s model. The results indicated that ice thickness varied spatially and temporally, and Stefan’s model overestimated the ice thicknesses with a maximum discrepancy of 12 cm. Moreover, the calculated ice thickness was correlated with temperatures, variations of temperature and accumulated freezing degree days (AFDD) based on Pearson correlation analysis, showing that ice thickness was proportional to AFDD with a coefficient of 0.89, and negatively correlated with T0, T−3, and Tice(c) with coefficients of −0.69, −0.73 and −0.62, respectively. Therefore, linear and non-linear models were proposed, which were validated using datasets from three stations in Russia and Finland, demonstrating that the linear model incorporating AFDD and T0 can capture local ice freezing and thawing processes with a relatively minor discrepancy, and the results were consistent at different stations. The paper provides an approach to comprehensively study the ice formation process and a practical model to calculate local ice thickness.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.038 | 0.017 |
| Insufficient payload (model declined to judge) | 0.195 | 0.187 |
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".