In-situ Soil Temperature Data (2 and 10 cm) in Agro-forested Areas of St-Marthe and St-Maurice for 2020-21 and 2021-22
Bibliographic record
Abstract
In this dataset, In-situ soil temperature measurements were collected for two consecutive years in Agro-forested areas of St-Maurice and St-Marthe located in south Québec (Canada), from mid-October to the end of April, covering the periods of 2020-21 and 2021-22. 8 and 10 temperature plots were instrumented in St-Maurice and St-Marthe, respectively, to monitor soil freeze-thaw (FT) states. At each plot, five soil pits equipped with two soil temperature sensors at near-surface (2 cm) and 10 cm depths were installed along a cross shape with 5 m between each soil pit. We used the standard normal distribution, which is a continuous probability distribution, to determine the probability of soil freezing at each instrumented plot. As part of this dataset, Sentinel1 Interferometric Wide Swath Mode (IW) imagery was used, using both ascending and descending orbits during the time frame of early October to early June, covering the periods of 2020-2021 and 2021-2022. Three change detection algorithms were also calculated to retrieve the FT state for both VV- and VH-polarizations at the studied plots including freeze-thaw index (FTI) algorithm, backscatter differences (Delta), and exponential freeze-thaw algorithm (EFTA). For more detail please refer to the README file in dataset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".