Focalisation des ondes radio pour un Internet des objets efficace en énergie
Bibliographic record
Abstract
7 Z REE N°2/2019 LES GRANDS PRIX 2018 DE LA SEE Les defis de la 5e Generation (5G) de reseaux Au debut des annees 2010, l’industrie des reseaux sans fil est en alerte et anticipe un deferlement massif de plusieurs dizaines de milliards d’objets p urs deferleme nnectes a l’horizon des annees 2020 [1]. C’est dans ce contexte alarmant conne C’est d zon des annees que demarre le projet europeen METIS 2020 sur la 5G [2] et que les prin qu sur la et europeen METIS - cipaux operateurs de reseaux mobiles du monde quantifient les objectifs a nde quan eseaux mobiles du atteindre par la future 5G : : « La 5G devrait supporter 1 000 fois plus de trafic dans les 10 prochaines fois plus de e trafic dans 0 p 1 000 fois annees, avec la moitie de la consommation d’energie dans tout le reseau mation d d’energie d out le de la c qui est aujourd’hui consommee typiquement, dans les reseaux d’aujourd’hui. nt, dan ans les res d’aujo mmee typiquement, d Ceci necessite que l’efficacite energetique soit amelioree d’un facteur 2000 meliore eli n facteur energetique soit am dans les dix prochaines annees » [3]. 3] Le cahier des charges
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".