Bibliographic record
Abstract
An in-depth underwater inspection revealed extensive cracking and low-strength concrete within the seals of all intermediate pier foundations of the U.S. 70 bridge that crosses Lake Hamilton in Hot Springs, Arkansas. After consideration of several rehabilitation solutions, the Arkansas State Highway and Transportation Department recommended and selected a micropile rehabilitation option as the most cost-efficient way to transfer loads from the pier footing directly to the rock below, bypassing the seal concrete. Micropiles are high-capacity drilled piles, usually in the range of 5 to 12 inches in diameter, that can reach depths greater than 200 ft and achieve working loads that exceed 200 tons. In the Lake Hamilton project, the designed micropile was comprised of 7-in. diameter, 1.5-in. thick, Grade 80 steel casing; a 2-in. thick, Grade 80 reinforcement bar; and 5,000-psi non-shrink grout with a minimum embedment depth of 17 feet. A total of 152 micropiles were installed. Because of their small diameters, the micropiles could be installed through the existing foundation and grouted into place. Specialized drilling equipment enabled the easy conversion to a larger diameter casing and allowed the contractor to drill through the existing reinforced foundations and a debris layer without the hole caving in.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".