A Proposed Model for Adoption" High Technology Products (Robots) for Indian Construction Industry
Bibliographic record
Abstract
A Proposed "Model for Adoption" High Technology Products (Robots) for Indian Construction Industry S. Jain, M. Phadtare Pages 110-118 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Construction industry is considered as labour intensive, having shortage of skilled labour, unsafe with large number of industrial accidents. Construction industry requires high technology automation products (Robots) for improving productivity, safety, quality etc. Robots are developed by various countries in different areas like demolition, earthwork, bridge, tunnels, road work, underwater works, trenches and piping, maintenance etc. however they are still not used to their full potential by construction industry. Hence in this paper, the authors propose a "model for adoption" of robots in construction industry. This model considers how a construction firm will adopt full scale robots like manually controlled machine, tele-controlled machines, computer controlled machines and cognitive robots and assimilate them through various stages. Keywords: High technology products, robots, construction industry, adoption, India, SAM DOI: https://doi.org/10.22260/ISARC2013/0012 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".