Discussion of “Framework to assess Newmark-type simplified methods for evaluation of earthquake-induced deformation of embankments”
Bibliographic record
Abstract
Rationale for filtering recorded seismic data at the dam crest to remove high-frequency components is not clearconsidering that the dam material acted as a filter to the propagating wave, the recorded data should be usable as is.As such, we attached a greater credence to the PCA of 1.65g than to the PCA of 0.8g. 3 This confusion regarding which of the two components (N-S or E-W) was actually used in Kan et al. (2017) is also observed in Kan and Taiebat (2016).Therein, the text indicates use of the N-S component but the Fig. 13 caption therein shows the plot of data using the E-W component.There is also a mention of filtering frequencies >20 Hz, and performance of base-line correction.However, a rationale for filtering and its effects on computed results are not given.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".