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Record W2342653641 · doi:10.22260/isarc2013/0012

A Proposed Model for Adoption" High Technology Products (Robots) for Indian Construction Industry

2013· article· en· W2342653641 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the ... ISARC · 2013
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsRobotAutomationComputer scienceWork (physics)Manufacturing engineeringDemolitionDownloadEngineering managementEngineeringCivil engineeringArtificial intelligenceMechanical engineeringWorld Wide Web

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.206
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it