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
UsingPassive Infrared Sensors with Low Resolution: A Systematic Review". 1 We hope our present answers will help readers to fully appreciate the article.Regarding the non-registration of our study in any international database of prospectively registered systematic reviews, we specified this information at the beginning of the section Materials and Methods, sub-section Protocol and Registration.2 This study was therefore not registered, for example, in the PROSPERO database.We agree with Priastana & Simbolon on the additional precautions brought by registration in this type of database particularly to avoid duplication of scientific effort.However, the affirmation "to be reviewed by peers" is not correct.Indeed, PROSPERO is a registration system (accessible on the website https://www.crd.york.ac.uk/prospero/), which help researchers to comply with PRISMA recommendations and improves transparency of the review process.No peer review is carried out by the PROSPERO team.The latest PRISMA guidelines 3 recommend to specify the registration information if the study was submitted, or, if it is not the case, to state that the review has not been registered, which we did (see also the PRISMA 2020 Checklist on the website http://www.prisma-statement
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 | 0.013 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.031 | 0.018 |
| Insufficient payload (model declined to judge) | 0.048 | 0.031 |
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".