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,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".