Global Wheat Dataset USask_1 subset Spikelet Annotation
Bibliographic record
Abstract
Spikelet annotation for 67 images taken from the Global Wheat Dataset USask_1 subset<br>If you use this data please cite the following papers<br><b>Unsupervised Domain Adaptation For Plant</b><b>Organ Counting </b><br><b>Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods</b><br><br><pre><code>@inproceedings{ayalew2020unsupervised, author={Ayalew, Tewodros and Ubbens, Jordan and Stavness, Ian}, title = {Unsupervised Domain Adaptation For Plant Organ Counting}, booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)}, month = {August}, year = {2020} } </code></pre><pre><code>@article{david2020global, title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods}, author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul Arifin and others}, journal={arXiv preprint arXiv:2005.02162}, year={2020} } @article{Ayalew2020, author = {Ayalew, Tewodros and Ubbens, Jordan and Stavness, Ian}, title = "{Global Wheat Dataset USask_1 subset Spikelet Annotation}", year = "2020", month = "7", url = "https://figshare.com/articles/dataset/Global_Wheat_Dataset_University_of_Saskatchewan_Spikelet_Annotation/12652973", doi = "10.6084/m9.figshare.12652973.v3" }</code></pre>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".