Bibliographic record
Abstract
The volume of the content has been published through ICAMBC_2021 (International Conference on Advanced Materials Behavior and Characterization_2021) conducted as virtual online conference on 24-26, April 2021. The conference was organized by Mattest Research Academy, Chennai, Tamil Nadu, and India. The focus of the conference is to promote the innovative works from core and diversified fields of materials engineering for different applications. Materials engineering is one of the very most important interdisciplinary fields that focuses on studies fundamental physical and chemical basis in order to develop new materials and compounds through manipulating atoms to form desired structures and properties. Any revolutionary changes are always through the breakthrough in new materials. The manuscripts were submitted to the conference from multidiscipline of Materials, Engineering and Sciences. The authors and participants from international universities and National institutions and Research organizations were joined through online via Google Meet. The experts and session chairs have provided talk related to recent research innovations and importance of the virtual conference in this pandemic situation due to COVID’19. The forum comprised around six invited talks in the area of Bio sciences Medical sciences Recent Advanced Materials such as Nano Materials Polymers, Ceramics Natural Fiber Reinforced Composites, Phase Change Materials, Surface Engineering, tribology and biomaterials. The conference had received a good opening to the research society and everyone encouraged about the event was conducted for three days in pandemic situation. Each presentation is around 20-30 minutes as parallel sessions in front of subject experts. The interaction session provided the real experience to the participants and they had good exposure with the students and delegates from global Institutions. The global participants from all states in India, South Africa, Nigeria, Iran, Turkey, Morocco Ethiopia, Bangladesh, and Canada had presented and participated in the conference. Indeed it was a wonderful experience for the global researchers to share their research innovations and the feedback is the evidence from the participants registered in our website. https://www.mattest.net/feed-back Dr.I.Saravanan Convener-ICAMBC_2021 Mattest Research Academy, Chennai, Tamil Nadu, India. List of Editors, Committee Members are available in this pdf.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.563 | 0.420 |
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".