Bibliographic record
Abstract
LooK SPOT COVID-19 antigen rapid test uses a smartphone-based LooK SPOT reader, antigen cassette, and LocationNow AI Cloud to qualitatively detect the SARS-CoV-2 Nucleocapsid protein antigen within 5 minutes. The reagent is used to detect the SARS-CoV-2 nucleocapsid protein antigen, which is usually present in the upper respiratory tract sample in the acute phase of infection. When a positive result is presented, it means that there are viral antigens in the sample. The LooK SPOT system consists of 5 key components: LooK SPOT reader LooK SPOT app LooK COVID-19 antigen cassette LooK PASS app LocationNow AI Cloud To start the test, the patient has to download and register the LooK PASS app from the app stores. The patient scans the QR code of the LooK COVID-19 antigen cassette with the LooK PASS app, and the information is uploaded to the LocationNow AI Cloud for the registration of the test. Then place the nasopharyngeal swab with the sample of the patient into the Extraction Buffer Tube. Stir the nasopharyngeal swab in the Extraction Buffer Tube and wait for one (1) minute. The virus particles in the sample are disrupted and exposing the internal viral nucleoproteins. Use the liquid dropper to extract the liquid inside the tube and apply three drops in the sample window of the antigen cassette. Insert the antigen cassette into LooK SPOT reader attached to a smartphone by using the camera of the smartphone to take pictures from the sample. LooK SPOT reader detects the ID of the LooK antigen cassette and sends these images of the sample reading to LocationNow AI cloud for analysis. LocationNow AI cloud analyzes the images of the fluorescent signal of the sample by using proprietary AI algorithms and returns results of Positive, Negative, or Invalid to the LooK SPOT reader within 5 minutes. The same result is also sent to the patient's smartphone's LooK PASS app. This test procedure is secured with full privacy. LocationNow AI algorithms have reduced the false-negative cases caused by human visual check errors. LooK COVID-19 antigen rapid test is designed for use at the Point of Entry for the revival of the economy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.062 |
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".