Bibliographic record
Abstract
对农作物品种正确分类是作物分类学的重要内容，为考察X—ray成像技术对小麦品种分类研究的有效性，基于软X—ray成像仪采集的3品种（Kama，Rosaand Canadian）每个品种70个籽粒，共210个籽粒样本的x—ray扫描图像，并针对其7个形态几何特征（面积、周长、紧致度、籽粒长度、宽度、偏斜度、种子腹沟长度），提出了一种使用Kernel—ICA的方法先对特征进行优化，再进行小麦品种的聚类与识别的方法，并与K—means、C—means2种聚类方法以及基于工神经网络（ANN）和支持向量机（SVM）2种识别方法的分类结果进行比较，结果发现：分类正确率从高到低分别为：Kernel—ICA、SVM、C—means、K—means、BP—ANN，分类正确率分别为：91．9％、90．5％、89．5％、87．1％、86．9％。研究提出的Kernel—ICA的方法，聚类优化和识别能力较强，对软X—ray成像的小麦品种进行分类，已基本上满足农艺上对小麦品种分类需要，对农作物种质资源鉴别和作物品种分类研究具有积极意义。
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".