Proceedings of the international workshop on limit state design in geotechnical engineering practice : Massachusetts Institute of Technology, USA 26 June 2003
Bibliographic record
Abstract
Limit States Foundation Design Code Development in Canada (D E Becker) Geotechnical Acceptance of Limit State Design Methods (J T Christian) Reliability-Based Design as a Decision-Making Tool (R B Gilbert) Comprehensive Design Codes Development in Japan: Geo-Code 21 Ver. 3 and Code PLATFORM Ver. 1 (Y Honjo) New Directions in LRFD for Soil Nailing Design and Specifications (C A Lazarte et al.) Practical Lessons Learned from Applying the Reliability Methods to LRFD for the Analysis of Deep Foundations (S G Paikowsky) Why Consider Reliability Analysis for Geotechnical Limit State Design? (K K Phoon et al.) Use of Finite Element Methods in Geotechnical Limit State Design (B Simpson & M Yazdchi) Implementation of the AASHTO LRFD Bridge Design Specifications for Substructure Design (J L Withiam) and other papers
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.035 |
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".