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.Copyright remains with the author(s) or their institution(s).Permission for reuse (free in most cases) can be obtained from RightsLink.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".