Global Wheat Dataset USask_1 subset Spikelet Annotation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.038 |
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 it