Ground‐motion model for Housner’s spectrum intensity based on a novel hybrid‐scenario approach
Bibliographic record
Abstract
We use a novel hybrid‐scenario approach to develop a ground‐motion model (GMM) for Housner’s spectrum intensity ( SI ) using estimates of pseudo‐spectral acceleration ( PSA ) from the authors existing GMMs based on a scenario approach proposed in the literature and predictor variables from the same database used to develop the PSA GMMs. These estimates of SI are used together with the predictor variables to develop a hybrid‐scenario GMM using mixed‐effects regression analysis. Because the GMM is based on predicted values of PSA , the aleatory variability from the regression is not indicative of the actual variability of observed values of SI . Instead, a hybrid‐scenario model for magnitude‐dependent between‐event, within‐event, and total aleatory standard deviations is derived from the PSA GMMs using the scenario approach. The predicted values of SI and its standard deviations from the hybrid‐scenario model are found to be relatively consistent with the residuals between these predictions and estimates of SI from the database (i.e. the observations). However, the values of SI predicted from a purely empirical GMM developed directly from the observations are different than those from the hybrid‐scenario model by up to a few tens of percent. These differences are the result of an insufficient number of observations resulting from bandwidth limitations of the database that lead to a bias in the empirical results. The near‐source standard deviations from the hybrid‐scenario GMM are found to be generally consistent with both those from the empirical model and those from the residuals between the hybrid‐scenario model and the observations. However, the far‐source standard deviations of the hybrid‐scenario model are smaller than those from these other methods.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".