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。
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 | 0.000 | 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) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".