Un réseau de senseurs pour identifier des mélanges complexes de COVs dans l'haleine humaine
Bibliographic record
Abstract
Lung cancer is one of the deadliest form of cancer in Europe, being the first and second cause of cancer death respectively for men and women. This high death toll has to be blamed on the lack of obvious symptoms in the early stages of the illness. Current diagnostic methods tend make the screening costly and difficult to organise at a large scale. Asymptomatic subjects and people in remote areas are rarely tested overall, leading to late discovery of the cancer and poor survival chances. There is therefore a need for a diagnostic method that could be used remotely while being simple enough to be used with little prior formation. Gas sensor arrays have properties fitting for the task. This thesis aims at creating and testing a sensor array in order to build a benchmark on which one can compare the discriminative power of different arrays. Several tasks will be performed simultaneously: The first is the establishment of a standardized test method of the metrological characteristics of commercial thick film sensors as well as experimental ones, and their qualities within a sensor network. The second is the integration of experimental sensors into a prototype gas sensor array consistent with the final purpose of the device. The third is the validation of the test method with the prototype electronic nose, which requires the reproducible synthesis of reference gas mixtures. It is also planned to use real breath from healthy persons and cancer patients as validation of the benchmark’s conclusions. The last task is about the processing and analysis of data and the identification and classification of samples in order to obtain a measurement of the array’s discriminatory power. This thesis is part of the PATHACOV research project, funded by Interreg France-Wallonie-Vlaanderen.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".