Bibliographic record
Abstract
Reasoning involves making a choice among a number of available alternatives such that some predefined criteria are satisfied. In the construction context, one of the crucial choices to be made is the selection of the optimum construction methods for various parts of a project. Construction research to date has approached this subject from two important but disparate directions. The first approach has been developing automated systems for schedule generation based on the physical characteristics of the designed facility as well as the construction site. The second approach has been developing systems that aid in method selection using techniques such as artificial intelligence, simulation and AHP. However, an effective schedule cannot be generated based solely on a project's physical characteristics without taking the construction method(s) into account. Also, once a method is selected, it should be integrated in the schedule development process. Over the past two years the authors have been addressing this issue in a research project aimed at developing an integrated system that incorporates both method knowledge as well as the physical aspects of a project in the schedule generation process. This paper presents some of the achievements to date as well as some issues that require further inquiry.
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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".