Bibliographic record
Abstract
About ICCT '23Intelligent Communication and Computational Techniques (ICCT'23), 3rd International Conference served as a platform for knowledge sharing about the recent trends and advancements in the field of networking and high-end data handling and how these domains are playing role in research and market development of the industries.It provided great opportunity for our students and faculties to interact and share ideas with the top-notch in the field face to face.This knowledge sharing inspired and thrilled many young minds and helped us bring collaborations and global partners to work together.This enabled us to solve challenging problems in our society so that we may contribute to our world.The whole idea of the forum was to exchange thoughts and ideas, transform those in real time to solve the problems.Conference created awareness in students about the importance of scientific research in related fields and synchronizing with product market.IEEE and IEEE Delhi section was the Technical Sponsor.Various Technical sessions were categorized under the tracks of Artificial Intelligence and Machine Learning, Blockchain and Security, Internet of Things, Cloud and Security etc.In ICCT'23 Conference researchers form different countries like USA, Canada, Spain, Italy, Morroco, China, Bangladesh, Sri Lanka contributed in the field of research by submitting their papers.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.621 | 0.611 |
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; the direct Gemma label and the distilled Codex classifier 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".