Calibration and validation of a common lambsquarters (<i>Chenopodium album</i>) seedling emergence model
Bibliographic record
Abstract
Studies were conducted to calibrate and validate a mathematical model previously developed to predict common lambsquarters seedling emergence at different corn seedbed preparation times. The model was calibrated for different types of soil by adjusting the base temperature of common lambsquarters seedling emergence to the soil texture. A relationship was established with the sand mineral fraction of the soil and was integrated into the model. The calibrated model provided a good fit of the field data and was accurate in predicting cumulative weed emergence in different soil types. The validation was done using data collected independently at a site located 80 km from the original experimental area. There were no differences between observed and predicted values. The accuracy of the model is very satisfactory because the emergence of common lambsquarters populations was accurately predicted at the 95% probability level. This model is one of the first to take into consideration seedbed preparation time and soil texture. This common lambsquarters emergence model could be adapted to model other weed species whose emergence is limited by low spring temperature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".