Bibliographic record
Abstract
International Conference on Sustainable Practices in Engineering & Technology (IC-SPET’23) is the AICTE sponsored first international Conference organised by Department of Civil Engineering of Sree Buddha College of Engineering, Pattoor, from 14th to 16th June, 2023 with a focal theme on Sustainability in Civil Engineering. The sub themes were Structural Engineering & Sustainable Construction Technology, Water Resource & Environmental Engineering, & Transportation Engineering, Modelling & Computational Techniques in Civil Engineering and GIS application in Civil Engineering. The conference is planned and organized with an objective of providing a platform and conducive environment for the participants to share innovations in Engineering and Technology and to form international relationships among the researchers involved in Engineering and Technology. The three-day international conference was conducted from 14th June, 2023 to 16th June, 2023 in hybrid mode. The conference was inaugurated by the chief guest Dr. N.P. Rajamane, Professor, Scientist, Founder & Former Head (a) Centre for Advanced Concrete Research (CACR), SRM IST (b) Advanced Materials Lab (AML), CSIR-SERC. Renowned national and international speakers such as Dr. N. P. Rajamane (Scientist, Founder & Former Head - (a)CACR, SRM IST (b) AML, CSIR-SERC), Dr. Kamal Laksiri (Governor, Region 10, ASCE), Dr. Sumi Siddiqua (Associate Professor, The University of British Columbia, Canada), Dr. Tanmay Basak (Professor, Indian Institute of Technology Madras) and Dr. Vinu Unnikrishnan (Assistant Professor, College of Engineering, West Texas, USA) has delivered keynote address on sustainable practices in diverse fields of engineering. To IC-SPET’23, 103 technical papers from various institutions within and outside the state were submitted, out of which 65 papers accepted for presentation. And 17 papers were selected to include in this proceeding of conference through meticulous review process performed by experts in the field. We would like to greatly acknowledge the active contribution of keynote speakers, session chairs, reviewers, authors and participants towards the success of the conference. We express our indebted gratitude to Prof. K. Sasikumar, Hon'ble Chairman of the Sree Buddha Group of Institutions, Prof. V. Prasad (Secretary) and Sri. A. Sunil Kumar (Treasurer) of the Sree Buddha Educational Society, Dr. K. Krishnakumar, the Principal of Sree Buddha College of Engineering and all members of Sree Buddha Educational Society for their invaluable guidance and support for the successful coordination of the event. We appreciate the efforts of the faculty members, staff, and student volunteers of the Department of Civil Engineering, who worked as a team and shared responsibilities for the successful coordination. We would like to acknowledge the financial assistance received from AICTE (All India Council for Technical Education) for organizing the conference. We express our sincere gratitude to all the associating organisations and institutions such as ASCE, IEI Kollam local center, ICI Kochi center, Habilete learning solutions, Indian Institute of Infrastructure and Construction, Qcrete and T. R. Associates for their timely assistance. Finally, we express our sincere thanks to the team of IOP Conference series lead by Cat Leyland and Rachelle Morris for bringing out this proceedings.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.603 | 0.443 |
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".