Experience of evaluation of the array of ice data based on the annual course of insolation at the upper boundary of the atmosphere
Bibliographic record
Abstract
This article examines the reliability of ice data presented in the HadISST1 database (Hadley Centre Sea Ice and Sea Surface Temperature data set) of Hadley Meteorological Center (Hadley Centre for Climate Prediction and Research, Met Office, UK). The criterion for the reliability of HadISST1 database on the Northern hemisphere serves the average multiyear annual course of insolation in the Northern Hemisphere and the average multiyear annual course of insolation in 5-degree latitudinal zones at the upper boundary of the atmosphere, phase-shifted by two and three months to the past. It is revealed that the representative criterion for assessing the reliability of the business database is the average multiyear course of insolation in 5-degree latitudinal zones shifted by two months to the past. Evaluation of the data array on the Northern Hemisphere on the basis of the selected criterion demonstrates the overall high reliability of ice data reflected therein. However, there are two areas that differ in the degree of reliability of the presented data: 1) pack, drift ice with high negative values of the correlation coefficient; 2) coastal zone, in which the correlation is virtually absent (shore ice and flaw leads). The weak (or absent) correlation is observed over a significant length of the coastal zone in the Arctic Ocean, as well as in the straits and bays of the Canadian Arctic Archipelago. It is established hat the correlation coefficient of the annual course of the ice flow in the cells of the array of size 1 with the annual course of insolation of the Northern Hemisphere and the corresponding 5-degree latitudinal zones increases from the past to the present. This indicates the need for the correction of ice data in the early time range of the array (1901–1978), which precedes the time of the beginning of satellite observations.
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 | 0.032 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".