The use of artificial neural networks to predict the spatial variability of grain quality during combine harvest of wheat.
Bibliographic record
Abstract
s XVII.th World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR | SCGAB) (pp. 28), Quebec City: QC, Canada, 2010. [14] Stewart C.M., McBratney A.B., Skerritt J.H.: Site-specific Durum wheat quality and its relationship to soil properties in a single field in Northern New South Wales. Precision Agriculture, 2002, 3, 155-168. [15] Taylor J.; Whelan B.; Thylen L., Gilbertsson M.; Hassall J.: Monitoring wheat protein content on-harvester Australian experiences. In B. J. V. Stafford (Eds.) Precision agriculture '05, 5th European Conference on Precision Agriculture, Conference paper (pp. 369-375), Uppsala, Sweden, 2005. [16] Thylen L., Gilbbertsson M., Rosenthal T., Wrenn, S.: Sorting of Grain on the Farm Experiences with an Online Protein Sensor. In B: D.E. Maier (Eds.) International Quality Grains Conference (pp. 1-8), Indianapolis: Purdue University, USA,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".