Development of Classification Model for the Level of Bid Price Volatility of Public Construction Project Focused on Analysis of Pre-Bid Clarification Document
Bibliographic record
Abstract
Development of Classification Model for the Level of Bid Price Volatility of Public Construction Project Focused on Analysis of Pre-Bid Clarification Document Yeeun Jang, June Seong Yi, Jeongwook Son and Jeehee Lee Pages 1245-1253 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: The purpose of this paper is to classify the level of formation of the bid price by using the type of uncertainty inherent in the bid document as a variable. To this end, the research examined the factors of the project related to the bid price presented in the previous study. Next, the pre-bid clarification document, which can be used to check the uncertainty of the bid documents, is used as a surrogate variable. Through these input variables, this research implemented two kinds of models using four algorithms: one predicts the level of bid price with uncertainty of bid document and the other predicts the level of bid price without uncertainty of bid documents. As a result, the model that predicts the level of the bid price reflecting the uncertainty of the bid document shows about 24 percent better performance than the model that predicts the bid price without reflecting the uncertainty of the bid document. Keywords: Risk Management; Bid Price Risk; Bid Price Volatility; Uncertainty of Bid Document; Pre-Bid Clarification; Bid Price Average; Bid Price Range; Machine Learning (ML); Classification Model; Public Construction Project DOI: https://doi.org/10.22260/ISARC2019/0167 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".