Bibliographic record
Abstract
Artificial intelligence has achieved a breakthrough with the proposal and development of deep learning. Compared with traditional models, deep learning allows machines to extract features and train neural networks by learning weight parameters. Convolutional Neural Networks (CNN), as the top priority of deep learning, have achieved remarkable results in 2D image recognition and classification segmentation. Recently, points cloud is a recent hot 3D data form in the field of deep learning. Point clouds retain better spatial geometric information than other forms of 3D data such as mesh depth maps. Due to the disorder, rotation invariance, the uneven density distribution of 3D point clouds, high sensor noise, and complex scenes, deep learning of 3D point clouds is still in the initial stage, and there are significant challenges. The tasks of deep learning for point clouds are mainly classified into shape classification, instance segmentation, semantic segmentation, etc. This article specifically outlines the development of methods for shape classification tasks and the characteristics and differences of each method. In addition, a comparison of the training accuracy and efficiency of each method on the dataset is provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".