No‐Tillage had Warmer Over‐Winter Soil Temperatures than Conventional Tillage in a Brookston Clay Loam Soils in Southwestern Ontario
Bibliographic record
Abstract
Core Ideas Tillage practices on surface soil temperature in the winter months were tested. No‐till soil responses slowly to changes of air temperature than tilled soil. Soil temperatures in winter were significantly greater in no‐till than tilled soils. Soil temperature affects soil microbial activity and hence impacts soil greenhouse gas (CO 2 , N 2 O, CH 4 ) emissions and plant nutrient recycling. However, there is a lack of information on the effects of tillage practices on soil temperature in the surface soil layers in the winter months. Soil temperature and moisture in no‐tillage (NT) and fall moldboard plow (MP) of a Brookston clay loam in southwestern Ontario were measured on an hourly basis over the winter months (December to April) at soil depths (25, 75, and 150 mm) during 2012 to 2013, 2013 to 2014 and 2014 to 2015, respectively. Both soil temperature and moisture significantly varied with tillage practices over the winter months. The tillage and depth interaction occurred for soil moisture, but not soil temperature. Soil temperatures in December and January were significantly greater (+0.7°C) in NT than MP soil with a maximum divergence of 2 to 4°C in the 25‐mm depth for 4 to 5 d in January. In March and April, the soil temperature was about 0.7°C cooler for the NT than the MP soil. The soils were generally wetter in the NT than the MP plots and the difference was statistically significant from December to February. In general, the soil was warmer and wetter under NT than MP management in winter months for this clay loam soil whereas soils were cooler and wetter under no‐tillage in the spring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".