Keeping Better Site Records Using Intelligent Bar Charts
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.
Bibliographic record
Abstract
Daily recording of the actions done by all parties on a construction site is necessary, not only for confirming that work is done according to specifications, but also for analyzing any claims for additional time/cost. Site records, however, are often incomplete and inaccurate, and commercial scheduling software provides little support in this regard. In this paper, a simplified approach for site-data recording and constructing “as-built” schedules is introduced through the use of intelligent bar charts. The proposed bar chart guides the user through progress reporting by observing any conflict with the planned logic of the work. It automatically recognizes the occurrence of delays and asks the user to record the responsible party and the reasons. Based on percent completes and recorded delays, the bar chart recognizes the progress status of activities as being slow, suspended, or accelerated. The paper starts with a description of the types of data that need to be recorded on site. It then provides a description of the automated guidance mechanism of the proposed bar chart, along with details on schedule integration and applicability for claim analysis.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it