Advanced medical collaborative technologies---Laboratory for collaborative diagnostics
Bibliographic record
Abstract
The Laboratory for Collaborative Diagnostics works in the field of Collaborative Diagnostics. A definition of Collaborative Diagnostics is found at www.lcd.utoronto.ca. The Malaria TV project is an application of our Collaborative Digital Microscope project described in greater detail at the link (www.lcd.utoronto.ca/collaborativedigitalmicroscope.html).The Malaria TV project will give participants a sense on how public health tools can be designed, developed and delivered using: 1)collaboration technologies like Access Grid; 2)commodity or cheap computation gear and 3)generic public health lab microscopes and CCD cameras.To demonstrate the tool's effectiveness the participants will be able to interact with a real live parasitologist who will interpret and explanation biological samples. We will also be attempting to link in labs from other parts of Canada and Africa.The session will give participants a sense on how collaboration technology can be used to address very real problems.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.100 | 0.060 |
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".