Performance of Chickpea-Flax and Pea-Mustard Intercrops at Two Saskatchewan Sites in 2019.
Bibliographic record
Abstract
Intercropping is when two or more crops are grown together in the same field within the same growing season. There are numerous benefits to intercropping, some of which are increased yield of one or both crops, yield stability or reduced risk of crop failure, lower input costs of commercial fertilizer and pesticide applications. This is of interest to organic producers, conventional producers, and a growing number of producers who wish to change their mode of production from conventional to low-input agriculture. In the temperate growing zones the most popular intercrop combination is a legume with a non-legume. Legumes are good choice for intercropping due to their ability to fix nitrogen, which reduces their competition for nitrogen in an intercrop system. Intercropping with legumes could also achieve improved nutrient recovery from existing soil reserves. There is currently no published data on pulse/oilseed intercrops in western Canadian soils. My research addresses the need to document and understand the operative processes and mechanisms in pulse/oilseed intercrops through an initial exploration of the impact of the legumes and oilseed crops grown together and separately on grain production and nutrient cycling and availability. This information may assist in the implementation of effective of pulse/ oilseed intercrop systems in western Canada.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".