Bibliographic record
Abstract
The purpose of this research was to compare the personality characteristics and driving behavior between the risky and safe drivers of Marivan Township.The statistical population included all drivers of Marivan township who had certification in 2014 that 225 persons of the statistical selected drivers were replaced in two groups of safe drivers (lack of accident and using of car insurance coupon) and risky drivers (accident record and using of insurance coupon) purposefully by referring to the insurance centers and according to the available sampling.The research variables were assessed through emotional intelligence questionnaire of Brad Berry & Jane Greaves and the questionnaire of Manchester driving behavior.The findings of the research questionnaires were analyzed by using of independent T-test and Hotelling , s T-test.The results of the comparative analysis showed, there was meaningful difference between the relations management and social awareness in risky and safe drivers.The rate of mistakes, errors, intentional and unintentional violations in risky drivers was more than the safe drivers and this difference was meaningful statistically.The results of this research showed that the personality characteristics and psychological components (emotional intelligence and driving behavior) have been different between the drivers and therefore these factors should be also considered in giving the certification to the drivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.941 | 0.929 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".