Notice bibliographique
Résumé
I woke this morning to the sound of my personal robotic assistant gently reminding me of the time.It was almost 7:30 and I had to be at my first meeting by 9:00.My assistant brought coffee to my room and replayed a brief summary of the overnight news headlines to let me know what was happening in the world."Excellent!"I thought as he re-counted how many new medals the U.S. had picked up at the 2024 Olympic Games overnight.After getting dressed, I went downstairs and turned on the 3D projector to watch more sports coverage while I enjoyed my breakfast.I ruminated about how strange it must have been for people to watch television through little flat windows on the wall instead of having a holo-projection filling the entire room."Right on time" I thought as I checked my bio-feedback watch and moved from the kitchen to my studio office.Sitting down in my chair, I turned on my computer and started the virtual conferencing application that would carry me to the meeting room at the company headquarters in Portland.The projector in my office also came to life and an entire meeting room, including a conference-room table, appeared in front of me.I watched as other members of my team popped into view at various seats around the table.Thanks to the light-field camera array pointed at my chair, I also now appeared virtually in front of everyone else at the meeting.We could all see each other in full size and depth and look around the entire room, making it almost indistinguishable from really being there.But, in fact, we were all in different places including China, Europe, the U.K., and the U.S.The meeting began with a holo-projection of the new product being assembled on the production line.I could see every detail of how the assembler was building the product and what issues he encountered.It was not going well enough and took too long to complete.We went around the room brainstorming about how to make the assembly easier and increase production to the critical target the GM wanted us to hit.After we collected the ideas, our animation artist used those ideas to re-build the original assembly sequence and then we could see exactly how the new assembly would flow based on our changes.A few more bugs needed to be worked out and then the final sequence was sent instantly to the factory floor, where the assembly team was waiting to watch the new projection.It took about 10 more minutes for all the assembly procedures to be electronically re-imaged and then we could see that we had solved the problem.Just as the meeting was breaking up, my wife messaged to say that our son the gymnast was just starting his morning warm-ups.He had a meet later that day that I was going to attend but I needed to work that morning.So, she used her portable light-field camera to capture his routines and they appeared on my office holo-projector, replacing the conference room that was just there earlier.As I finished my morning reports, I could keep an eye on him and even message back some "helpful" suggestions that made him wave back in embarrassment.The weekend before, our daughter played her first soccer game and the entire game was recorded with light-field cameras.After the game, the coach brought us all back to a holo-studio where we could replay all the critical moments of the game and literally walk onto the field in the projection and observe the plays from different angles.She could see and interpret her own foot work, understanding much better how to 2 Information Display 4/16
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,002 |
| Communication savante | 0,012 | 0,007 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,105 | 0,084 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».