Utilisation et apport des technologies géomatiques de pointe dans le secteur de la pêche aux îles de la madeleine
Bibliographic record
Abstract
Les technologies de l'information prennent de plus en plus de place dans les activites tant familiales que professionnelles. Certains secteurs, tel celui des peches, sont touches par l'emergence des technologies et par un marche qui propose sans cesse de nouveaux equipements a la fine pointe. Ainsi, les principaux acteurs doivent prendre d'importantes decisions quant a l'investissement dans de telles innovations. Cet article presente les resultats d'une recherche portant sur les conditions d'utilisation de technologies de localisation par les pecheurs des Iles de la Madeleine. Une enquete realisee aupres d'eux a permis de connaitre davantage leur profil demographique ainsi que le niveau d'adoption des technologies geomatiques. Les resultats obtenus indiquent que l'avantage relatif percu et l'image projetee lors de l'acquisition de systemes d'information geographiques jouent un role predominant lors de la decision d'en faire un usage courant.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".