Modeling site-specific wild oat (<i>Avena fatua</i>) emergence across a variable landscape
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The spatial and temporal pattern of wild oat emergence in eastern Washington is affected by the steep, rolling hills that dominate this landscape. The objective of this study was to assess the impact of landscape position and crop residue on the emergence phenology of wild oat. Emergence of a natural wild oat infestation was characterized over two growing seasons (2003 and 2004), at two wheat residue levels (0 and 500 g m −2 ), and at five landscape positions differing in slope, aspect, and elevation in a no-till winter wheat field. Wild oat emerged 1 to 2 wk earlier at south-facing landscape positions than at north-facing landscape positions. Crop residue delayed wild oat emergence by 7 to 13 d relative to bare soil at south-facing positions in 2003 and had a reduced effect on emergence at north-facing landscape positions. Therefore, preserving surface residues tended to synchronize emergence across the landscape and may facilitate better timing of weed control where residue is present. Emergence of wild oat was modeled as a function of thermal time adjusted by water potential using a Weibull function. Temperature explained more variation in the model than water potential. This model explained much of the variability in wild oat emergence among landscape positions over these 2 yr and may be useful as a tool to predict the timing of wild oat emergence. Results also indicate that site-specific modeling is a plausible approach to improving prediction of weed seedling emergence.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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