Efforts to Unmanned Construction for Post-disaster Restoration and Reconstruction
Bibliographic record
Abstract
Efforts to Unmanned Construction for Post-disaster Restoration and Reconstruction Shigeo Kitahara, Yasushi Nitta and Shigeomi Nishigaki Pages 1155-1162 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: This paper presents largely severe natural disasters had happened in Japan, and efforts to unmanned construction until now. First, problems in responses to post-disaster restoration and reconstruction are reported. Secondly, are described demonstration of ultra-long-distance unmanned construction and the requirements for the deployment. Thirdly, this paper presents research and development on autonomous crawler carrier. Finally, concluding remarks and further works are reported. In addition, are proposed levels of promising applicability of robots in responses to post-disaster restoration and reconstruction. Keywords: Post-disaster; Unmanned construction system; Situational Awareness; Ultra-long-distance; Autonomous crawler carrier DOI: https://doi.org/10.22260/ISARC2019/0154 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".