Bibliographic record
Abstract
Gears exhibit vibrations during operation which contain apparently random components. Due to time-varying stiffness and backlash nonlinearity, it is very hard, if not impossible, to get a closed form response of gears under combination of deterministic and random loads. This paper employs a numerical method termed as path integration to obtain the probability density of the response at discrete time instants. The random excitation is assumed to be White noise. The response of the gear is a Markovian processes under the random excitation. In order to capture the probability density evolution, discretization is applied to both space and time. The transition probability between adjacent time instants is assumed to be Gaussian and the calculation of the probability density is made on a reduced finite space. The mean and variance, which are used to construct the Gaussian distribution, are obtained through a direct numerical integration scheme with one stochastic Newmark method. Statistic linearization technique is utilized within each individual time-step to find a linear equivalent of the original system with backlash nonlinearity. Through this method the evolution of the probability distribution function of the response displacement and the velocity is calculated. Three representative cases with different levels of constant load are investigated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".