Notice bibliographique
Résumé
There I was, 21 years old, standing at the base of the tower and staring up 33 floors to the top of Shell Centre. Kind of symbolic—starting my career and visualizing the long climb ahead. Just another new graduate from the University of Saskatchewan working in Calgary as a reservoir engineer. I felt daunted by all the very smart and very experienced people around me. And, like all my young and eager friends, I too wanted to load up my resume with as much experience as possible, and as soon as possible. But how to do that? The big projects that we worked on as parts of a multidisciplinary team were exciting and great learning opportunities, but they developed over a long time, and time is something young folks are impatient about. But as part of my normal work routine, I soon was exposed to the amazing treasure trove of SPE publications. I found that if you read a paper carefully, and followed its trail of references, and then followed the references in the references, you started to get a good perspective on the technical breadth and depth of our industry. If you read all the papers on a sub-topic, it would lead you to another related topic, and so on. Even though Shell, for example, had an excellent library of technical materials and case histories, nothing could match the SPE paper archives. So I started to use the SPE library as my own personal "experience accelerator." Getting into that mountain of SPE papers might appear an overwhelming task, if you think about it. The society has a library of almost 50,000 papers, which is why my advice to young engineers is not to think about it. But for argument's sake, let's say that about 20% of the papers pertain to your particular field of interest. At one paper per day, and allowing for weekends and vacations, it would take you only 40 years to get through the papers in your technical area ("Forty years? Are you kidding me?") OK, say that only about 20% of the papers in your technical area are the ones you really need to read, but let's also remember that reading and properly digesting even two or three papers per week is a lot of work. So realistically, it might take you only 15 years of reading ("Fifteen years?" "Yeah, that's right, kid, and unfortunately, another 20,000 papers will get published while you were reading, so don't expect to ever get finished. Ever hear of Sisyphus?").
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 | 0,005 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,015 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,023 | 0,016 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,448 | 0,412 |
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 ».