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
Foundations of existing highways and over-river bridges may have significant functional value. Hence, reuse of foundations of existing bridges during reconstruction or major rehabilitation can result in significant savings in costs and time. This report on bridge foundation reuse addresses critical issues encountered during decision-making on foundation reuse, assessment of existing bridge foundations for integrity, durability and capacity, strengthening of bridge foundations / substructures and design of bridge foundations for future reuse. The report includes numerous case examples on reuse of bridge foundations in the U.S. and Canada to highlight significant benefits of foundation reuse from social, environmental and economic perspectives. These case examples also present a detailed process followed in resolving integrity, durability and capacity issues encountered during the reuse process, and will serve as a knowledgebase for transportation agencies interested in reusing bridge foundations. Planning for reuse during the construction of a new bridge is a very important sustainability initiative that has also been addressed in this manual. This document is not meant to be used as a guideline; only as decision-making tool in addressing technical challenges and risk in reusing bridge foundations.\n
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it