Effect of nitrogen fertilizer and landscape position on wild oat (Avena fatua) interference in spring wheat
Bibliographic record
Abstract
Site-specific fertilizer application is of primary interest among those investigating site-specific crop management. Site-specific soil moisture levels may be associated with the relative success of certain weed species, and weed competition can be affected by fertilizer application. A field study was conducted to investigate how landscape position–based site-specific nitrogen fertilizer application would influence wild oat interference in spring wheat. Wild oat was allowed to grow in spring wheat at foot and knoll landscape positions in the presence or absence of 80 to 90 kg ha−1 of spring preseed broadcast nitrogen fertilizer. In all three site–years of the study, landscape position did not affect wild oat competitiveness in wheat or relative wild oat biomass, and there were no significant landscape position by nitrogen fertilizer interactions for these variables. The lack of landscape position effect may be attributed to the lack of substantive differences in soil characteristics between landscape positions at the sites used in this study and to normal to above-average seasonal precipitation levels in all three site–years. Wild oat competitiveness in wheat was significantly greater in the presence of nitrogen fertilizer for all three site–years. The results of this study suggest that, when there are not great differences in typical soil characteristics between landscape positions and precipitation levels are normal or above normal, landscape-based site-specific nitrogen fertilizer application does not affect wild oat competitiveness in spring wheat, but preseed spring broadcast nitrogen fertilizer does make wild oat more competitive in spring wheat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".