The Influence of Happiness Based on the Students of Neural Networks -An Empirical Study in Qinhuangdao
Bibliographic record
Abstract
Yu, W.; Zheng, X., and Han, W., 2020. The influence of happiness based on the students of neural networks -an empirical study in Qinhuangdao. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 275-278. Coconut Creek (Florida), ISSN 0749-0208.This article takes the Elderly University of Qinhuangdao, a coastal city in China as an example, adopting the Newfoundland Memorial University Happiness Scale to measure the student's happiness score, selecting five variables - the students' gender, living status, education level, major (department) and school learning time - to be used as the main factors of affecting the happiness of senior college students, to construct BP neural network and quantitatively explore the influence of each factor on their happiness. The results of the study show that female students' happiness is higher than that of male students, the level of their happiness is directly related to their living conditions, improved education can increase their happiness, their happiness is directly related to their major, and the longer students learn in school, the higher their happiness will be.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".