Bibliographic record
Abstract
force, 206 active earth pressure coefficient, 406 additive rules, 8 advanced estimation techniques, 189-202 aliases, 100 allowable stress design, 245 Anderson-Darling test, 174, 178 anisotropic correlation structure, 121, 371 aperiodic, 78 area of isolated excursions, 139, 143 arithmetic average, 151 arithmetic generators, 204 assessing risk, 241-244 asymptotic extreme value distributions, 63 asymptotic independence, 178 autoregressive processes, 107 averages, arithmetic, 151, 395 geometric, 58, 152, 395 harmonic, 155, 396 over space, 180 over the ensemble, 180 B balance equations, 82 band-limited white noise, 106 Bayes' theorem, 12, 67 Bayesian updating, 13 bearing capacity, 347-372 c -φ soils, 347-356 empirical corrections, 353 equivalent soil properties, 349, 361-362 lifetime failure probability, 359, 365 load and resistance factor design, 357-372 logarithmic spiral, 347, 358 mean and variance, 349-351 probabilistic interpretation, 354-355 probability density function, 354 probability of failure, 361-364 weakest path, 347 worst-case resistance factors, 369 bearing capacity factors, 347 Bernoulli family, 32, 43 Bernoulli process, 32 Bernoulli trials, 32, 212 best linear unbiased estimation, 127, 182 bias, 164, 357 binomial distribution, 34, 68, 212 birth-and-death process, 83 bivariate distribution, 21 lognormal, 59 normal, 54 block permeability, 270 bounded tanh distribution, 60 Brownian motion, 107, 111, 135, 198 C calibration of load and resistance factors, 249 going beyond calibration, 255 cautious estimate, 251 455 Risk Assessment in Geotechnical Engineering Gordon A.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.604 | 0.454 |
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".