Modelling soil temperature on the boreal plain with an emphasis on the rapid cooling period
Bibliographic record
Abstract
To accurately model soil temperatures on the Boreal Plain, factors that influence fine-grained \nsoils during the rapid cooling period must first be identified. The effects of air temperature, soil \nmoisture and snow depth were quantified at 0.1 and 0.5 m depths for 14 sites encompassing five \ntreatment types: three upland burned, three upland harvested, three upland conifer, three upland \ndeciduous and two wetland. In the absence of snow from September to October at the 0.1 m \ndepth, air temperature was identified as the most important parameter, explaining approximately \n70% of the variation in soil temperature for upland and wetland sites. At the same depth in the \npresence of snow from November to December, soil moisture was more important. At a deeper \nsoil depth (0.5 m), soil moisture was identified as the most important parameter regardless of \nsnow cover, explaining from 63 to 91% of the variation in soil temperatures for upland and \nwetland sites. The presence of snow was a significant factor influencing soil temperatures, but \nsnow depth was not. Further, the soil temperature algorithms of SWAT were tested using one \nsite of each treatment type at 0.1, 0.5 and 1.0 m depths. The algorithms utilized by SWAT were \nable to reproduce seasonal trends in soil temperatures adequately for the spring, summer and \nautumn seasons, with only a slight increase in the lag coefficient parameter. During winter \nmonths, the SWAT algorithms tended to predict soil temperatures that were consistently lower \nthan measured data. Further development to the SWAT soil temperature algorithms is required to \nrepresent better the important insulating effect of snowpack.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".