Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
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
This volume contains the proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2020), held from June 26-28, 2020. The keynote speakers who also further explored this topic that is so significant for this field include: Prof. Witold Pedrycz, IEEE FELLOW, University Of Alberta, Canada, on speech title "Rule-Based Architectures: A study in the Design of Granular and Scalable Interpretable Models", Prof. Alex Kot Chichung, FELLOW OF IEEE AND FELLOW OF IES, Nanyang Technological University, Singapore, on speech title "Skeleton based Human Action Recognition", Prof. Jin Li, Guangzhou University, China, on speech title "Blockchainbased Secure Data Sharing Platform in IoT", and Prof. Jixin Ma, University Of Greenwich, UK, on speech Title "The Most General Temporal Constraint for Causations".
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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