Can Stronger Family Connections Alleviate the Adverse Effects of Unemployment on Happiness? Evidence from Asian Countries
Bibliographic record
Abstract
The present study investigates the application of uncertainty modelling for the purpose of detecting pedestrianintentions in contexts pertaining to autonomous driving. The proposed framework integrates two mechanisms: threshold modulation networks for aleatoric uncertainty and cost-sensitive learning for risk-aware decision making.Experiments on the PIE dataset with ResNet50, VGG16, and AlexNet demonstrate that cost-sensitive learning enhances F1-scores marginally (0.05-0.58 percentage points) by prioritising recall for crossing pedestrians. ResNet50 demonstrates the strongest performance (98.30% accuracy, 96.35% F1-score), significantly outperforming more elementary architectures. Threshold networks have been observed to introduce computational overhead, resulting in approximately a doubling of training time, accompanied by slight performance reductions. The study provides empirical evidence for the trade-offs between uncertainty modelling complexity and classification performance in pedestrian intention detection, offering insights for designing safety-oriented perception systems with appropriate computational constraints.nbsp;
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".