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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".