Response to the Letter: “A Response to: Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review” [Response To Letter]
Bibliographic record
Abstract
Grégory Ben-Sadoun,1,2 Emeline Michel,3,4 Cédric Annweiler,1,5–7 Guillaume Sacco3,5,8 1Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, France; 2Normandie Université, UNICAEN, INSERM, COMETE, CYCERON, CHU Caen, Caen, 14000, France; 3Université Côte d’Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique du Cerveau et du Mouvement, Nice, France; 4Université Côte d’Azur, LAMHESS, Nice, France; 5LPPL, Laboratoire de Psychologie des Pays de la Loire, Univ Angers, Université de Nantes, EA 4638 LPPL, SFR CONFLUENCES, Angers, F-49000, France; 6School of Medicine, Health Faculty, University of Angers, Angers, France; 7Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada; 8Université Côte d’Azur, CoBTek, Nice, FranceCorrespondence: Grégory Ben-Sadoun, Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, France, Email gregory.ben-sadoun@unicaen.fr View the original paper by Dr Ben-Sadoun and colleagues This is in response to the Letter to the Editor
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".