Evaluation of Some Physiological and Quantitative Traits in Different Ecotypes of Linseed (Linum usitatissimum L.) Under Chemical,Organic, and Biological Nitrogen Fertilizers
Bibliographic record
Abstract
Nitrogen is one of the major macronutrients in cropping systems. Considering the effect of nitrogen on the quantitative and qualitative characteristics of linseed, a field experiment was conducted as factorial arrangement in a randomized complete block design with three replications in the Research Station of Faculty of Agriculture, Shahrekord University in 2012. Five fertilizer treatments of urea, Azomin, Nitroxin, Super NitroPlus and control (without fertilizer) and three ecotypes of Iranian, Canadian and French linseed in this experiment were examined. Harvest index, seed protein and oil contents (%) were evaluated. Meanwhile, the trend of the cumulative crop growth rate (CGR) and fitted regression model was studied. Harvest index was significantly different between ecotypes. Harvest index, seed protein and oil contents showed significant responses to fertilizer treatments. The interaction between ecotypes and fertilizer treatments was significant for harvest index and seed oil content. Non-linear regression model (peak) best fitted on the trend of crop growth rate (CGR) in different ecotypes and different fertilizer treatments. According to result, it seems biological fertilizer of Super NitroPlus, Nitroxin and organic fertilizer of Azomin are capable of being hired in sustainable agriculture as an alternative to chemical fertilizers in linseed cultivation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".