https://combinatorialpress.com/jcmcc-articles/volume-127a/research-on-green-logistics-network-planning-strategy-combining-machine-learning-and-carbon-emission-constraints/
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
The aim of this study is to construct a deep learning-based biomechanical model of musical instrument playing action that integrates skeletal pose estimation and action recognition techniques.PHRNet-based human pose estimation can extract the skeletal key points of a player from video data, and these key points provide basic data for instrumental performance action recognition and analysis.The human skeletal action recognition method based on diversity rewarded reinforcement learning framework (DDRL-GCN) classifies the extracted key point sequences into specific playing actions, and the musical instrument playing actions are successfully modeled.The biomechanical model of musical instrument playing action designed in this paper is applied to recognize the playing action of five different musical instruments, and the recognition accuracy can reach more than 90%.This paper is designed to distinguish between different musical instruments, the recognition effect is more satisfactory.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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