Evaluation of Some Physiological and Quantitative Traits in Different Ecotypes of Linseed (Linum usitatissimum L.) Under Chemical,Organic, and Biological Nitrogen Fertilizers
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it