CVA-ARMAV capabilities comparison over the Heritage Court Tower data
Bibliographic record
Abstract
In this paper a comparison beetween the Canonical Variate Analysis (CVA) and the Auto Regressive Moving Average Vector technique (ARMAV) over accelerometric measurements is performed. The data are relative to the Heritage Court Tower located in Vancouver, British Columbia and kindly shared to other researchers by Dr. C. Ventura. The measurements were performed by means of 8 accelerometers, placed all over the structure in five different configuration, by keeping two accelerometers in fixed positions as references. Frequencies and mode shapes obtained by means of our procedure are compared with those obtained by other authors also presenting their results at this conference. An indicator of the gaussianity of the signals is adopted in order to select the time histories and to improve the ARMAV estimations. The model order of the CVA method is selected using an automatised procedure based on Modal Assurance Criterion computation, which allows to reduce the interaction of the data analyst, which usually represents the crucial task of the CVA Procedure, to a minimum level. Mode shapes and frequencies well agree with those obtained by other methods, whilst damping estimation shows a consistent degree of inaccuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".