Additional file 1 of Scent dog identification of samples from COVID-19 patients – a pilot study
Bibliographic record
Abstract
Additional file 1: Additional video. Detection dog working with DDTS. The video (Additional file 1) shows the Labrador Retriever “Seven” during a detection session. The Detection Dog Training System (DDTS) can be seen at the bottom of the video. The scent hole with a sample of an SARS-CoV-2 infected individual is marked in green on the video (please note the green mark was not seen by the dog and was only used in the video as a visualisation tool for the viewer to demonstrate the dog’s search and detection behaviour). At each detection trial run only one hole is presenting the target scent with the other six holes presenting saliva samples from SARS-CoV-2 negative tested individuals. When the dog detects the target scent, the nose will be left within the hole for ≥2 s to indicate the detection. This will be recorded by the device. A beeping sound announces the food or ball reward, which is automatically ejected by the device, distracting the dog for a short time period. In the meantime, the device rearranges the sample presentation in an automatic and random fashion, presenting one other scent hole with a sample of a SARS-CoV-2 positive tested individual and six control scent holes with negative control samples. In the upper left corner of the video, one can see how the figures change depending on the detection behaviour of the dog (true positive [correct indication; n = 3], true negative [correct rejection; n = 8], false positive [incorrect indication; n = 0], and false negative [incorrect rejection; n = 1]).
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.776 | 0.101 |
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".