Effects of zinc fertilizer amendments on yield and grain zinc concentration under controlled environment conditions
Bibliographic record
Abstract
The application of zinc (Zn) fertilizer to lentil is an agronomic strategy that has the potential to improve yield and enhance grain Zn concentration. A pot study was conducted to determine if Zn fertilizer applied to three popular Saskatchewan lentil cultivars could increase yield and concentration of Zn in the grain. The effects of soil and foliar applied Zn forms, including ZnSO4, Zn chelated with EDTA, Zn lignosulphonate, and a control were evaluated. Forms of Zn were not found to significantly increase yield (P = 0.828) or grain Zn concentration (P = 0.708) in any of the lentil cultivars tested. Fertilization with soil applied ZnSO4 resulted in significantly (P < 0.0001) higher amounts of residual available Zn in the soil relative to other Zn treatments. Soil fertilized with ZnSO4 had 1.13 mg kg−1 diethylenetriaminepentaacetic acid (DTPA)-extractable Zn compared to 0.84 mg Zn kg−1 and 0.77 mg Zn kg−1 in the soil and foliar applied chelated Zn, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".