Bibliographic record
Abstract
Noncontact monitoring of neonates in the neonatal intensive care unit (NICU) relies on accurate region-of-interest detection (ROI) detection for various downstream tasks, such as vital sign estimation and motion detection.This thesis investigates ROI detection for the NICU under adverse vision conditions, such as complete darkness and full occlusion by blankets, whereas traditional methods, using colour imagery, only consider ideal vision conditions.Pressure and depth imaging were leveraged as alternative imaging modalities since they are not affected by these adverse vision conditions.This thesis develops techniques for multimodal spatial registration between pressure and colour images.This work also establishes the benefit of transfer learning from adult data under different pretraining and fine-tuning strategies.Lastly, this thesis demonstrates that it is possible to estimate full pose from neonates even when the patient is fully occluded, using a combination of pressure and depth imagery.I would like to express my deepest gratitude to my supervisor, Dr. Green, for his invaluable guidance, encouragement, and support throughout the course of my thesis.His expertise and dedication to my research have been a constant source of inspiration and motivation.I would also like to express my gratitude to my family for their love and support throughout this journey: my
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".