An Emergence Model for Wild Oat (<i>Avena fatua</i>)
Bibliographic record
Abstract
Wild oat is an economically important annual weed throughout small grain producing regions of the United States and Canada. Timely and more accurate control of wild oat may be developed if there is a better understanding of its emergence patterns. The objectives of this research were to evaluate the emergence pattern of wild oat and determine if emergence could be predicted using soil growing degree days (GDD) and/or hydrothermal time (HTT). Research plots were established at Crookston, MN, and Fargo, ND, in 2002 and 2003. On a weekly basis, naturally emerging seedlings were counted and removed from six 0.37-m 2 permanent quadrats randomly distributed in a wild oat–infested area. This process was repeated until no additional emergence was observed. Wild oat emergence began between May 1 and May 15 at both locations and in both years and continued for 4 to 6 wk. Base soil temperature and soil water potential associated with wild oat emergence were determined to be 1 C and −0.6 MPa, respectively. Seedling emergence was correlated with GDD and HTT but not calendar days (P = 0.15). A Weibull function was fitted to cumulative wild oat emergence and GDD and HTT. The models for GDD ( n = 22, r 2 = 0.93, root mean square error [RMSE] = 10.7) and HTT ( n = 22, r 2 = 0.92, RMSE = 11.2) closely fit observed emergence patterns. The latter model is the first to use HTT to predict wild oat emergence under field conditions. Both models can aid in the future study of wild oat emergence and assist growers and agricultural professionals with planning timely and more accurate wild oat control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".