Parameterization of the freeze/thaw discriminant function algorithm using dense <i>in-situ</i> observation network data
Bibliographic record
Abstract
The near-surface soil freeze–thaw (FT) transition is an important factor affecting land-atmosphere exchanges, hydrology and carbon cycles. Thus, effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research. Several approaches have been developed to detect the soil FT state from satellite observations. The discriminant function algorithm (DFA) uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced (AMSR-E) passive microwave satellite observations. Although it is well validated, it was shown to be insufficiently robust for all land conditions. In this study, we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection. We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference. Considering the distinction between ascending and descending orbits, two different sets of parameters were acquired for each frequency pair. The validation results indicate that the overall discriminant accuracy of the new function can reach 90%. We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product, and a reasonable consistency between them was found.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".