Lentil Performance in Response to Weather, No‐Till Duration, and Nitrogen in Saskatchewan
Bibliographic record
Abstract
Increased soil N in no‐till (NT) systems can interfere with biological dinitrogen fixation (BNF), stimulate vegetative biomass, and affect harvest index (HI) and yield of lentil (Lens culinaris L.). In the black soils of Saskatchewan, the effects of short‐term (5–7 yr: ST) and long‐term (28–30 yr: LT) NT practices on lentil were investigated in two experiments from 2006 to 2008. One trial tested the effects of NT duration on plant N and yield of five cultivars; the second examined the response of a late‐maturing cultivar to NT duration at four rates of N fertilization. In both studies, average grain yield in 2006 and 2007 (warm seasons) was 21% greater than in 2008 (cool season), although total rainfall and plant N content was similar in 2007 and 2008. Average soil available N over the years was 23% greater in LT than ST, but the cultivars had 33% less N and 28% less yield in LT than ST in a dry year (2006), and similar performance in LT and ST in the wet years (2007 and 2008). The 60 kg N ha−1 treatment in the second trial diminished the yield difference between LT and ST in 2006. The only BNF measurement in 2008 showed that the cultivars fixed 10% less atmospheric N2 in LT than ST in this year. Overall, cool and wet growing conditions stimulate vegetative biomass and lower HI and yield of lentil, whereas a possible reduction of lentil BNF in NT systems is less likely to affect lentil yield.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".