Bibliographic record
Abstract
La multiplication des capteurs et des objets communicants de tous types a significativement enrichi le contenu des systèmes d'information (SI). Cependant, ces sources, souvent non maîtrisées, peuvent être leurrées ou corrompues par un tiers qui falsifie les informations produites. Cela soulève des questions relatives à la confiance accordée tant aux informations qu'aux sources et systèmes impactés. Cet article aborde la sécurité des SI sous l'angle de la confiance dans les sources d'information. La définition puis l'évaluation de la confiance dans un SI sont introduits avant de proposer une modélisation des sources d'information. La confiance dans ces dernières est abordée au travers de deux caractéristiques (la compétence et la sincérité) dont la mesure permet d'évaluer la confiance. Une expérimentation basée sur plusieurs sources simulées à partir d'un jeu de données réelles montre la pertinence de l'approche, transposable à d'autres SI. Cette étude est appliquée à l'analyse des données de navigation d'un navire.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".