Macro-relationships between regional-scale field pea (Pisum sativum) selenium chemistry and environmental factors in western Canada
Bibliographic record
Abstract
Garrett, R. G., Gawalko, E., Wang, N., Richter, A. and Warkentin, T. D. 2013. Macro-relationships between regional-scale field pea (Pisum sativum) selenium chemistry and environmental factors in western Canada. Can. J. Plant Sci. 93: 1059-1071. A baseline study of cultivar, temporal (2004-2006) and spatial variability in field pea (Pisum sativum) selenium (Se) concentration was undertaken in western Canada based on six common cultivars (295 samples) grown in 35 variety trials. Selenium was determined by atomic absorption spectroscopy following a HNO3 digestion. Non-significant differences in pea Se concentration occurred due to cultivar and temporal variability. Trial site soil organic C, pH, cation exchange capacity, soil texture estimates, and classifications were recovered from Agriculture and Agri-Food Canada's Canadian Soil Information System database. Twenty-five percent of the pea Se variability was due to soil edaphic factors, particularly organic C and pH, this increased to 39% with inclusion of great soil group classification. The remaining variability was due to growing season weather conditions, with hotter drier summers leading to higher Se concentrations. Naturally Se biofortified pulses are available to be targeted to selenium deficient populations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".