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
As part of the St. Lawrence Seaway lock maintenance, the current practice is to perform concrete repairs entirely with reinforced concrete, using either ordinary concrete or high performance concrete (HPC) mixtures. However, with the recent advances in the field of ultra-high performance fiber-reinforced concrete (UHPFRC), the use of this new material is considered in view of improving the overall performance of repairs. The goal is to implement repairs capable of dissipating a lot of energy before breaking when a ship hits a concrete lock wall. Numerous rehabilitation materials and methods have been experimented in the past. They all were unsuccessful due to inadequate shear and impact strength characteristics of the repair materials used. These needs can be efficiently fulfilled with UHPFRC, with their superior mechanical properties and very high energy-dissipation ability. To analyze the in-situ behavior of UHPFRC, two main mixture designs were investigated: a 160-MPa mixture containing 3% of steel fibers and a 120-MPa mixture containing 3.5% of a steel fiber blend. Thick repairs with average depths of 700 mm were carried out during the winter shut down period, in very harsh climatic conditions (-12 °C, gusty wind). The performance exhibited by the repairs after a full year shows that UHPFRCs can withstand very effectively the impacts from the transiting vessels
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 | 0.000 | 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) | 0.001 | 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