Notice bibliographique
Résumé
The Way Ahead Interview - A conversation with energy investment banker Matthew R. Simmons. What was your first job, and what were your impressions of the oil and gas industry when you started working? I accidentally stumbled upon my first job. When I was in my second year at Harvard Business School, I reluctantly signed up for a course called Manufacturing Policy. I say reluctantly because of all of the courses I took in my first year, Manufacturing Operations was singularly the most boring. But during a conversation with a finance professor, he recommended this as the best finance course of the second year. A few days later, the school paper listed all the secondyear courses, and Manufacturing Policy was rated as the course with the hardest workload in all second-year courses, so it was with great reluctance that I signed up. Upon completion of the course, before our grades were issued, I was asked to meet with the professor—I was positive I had flunked the course. I was astonished when the professor asked if I would stay on and work at the school as a research associate to rework the case studies in this particular course. This detour is what stopped me from returning to Utah and becoming a commercial banker, which had been my chosen career path when I enrolled. As it turned out, several of the case studies I worked on during this two-year program were on oil companies. The first was on Phillips Petroleum betting its future on building the world's largest ethylene plant. I then did a case on a new refinery being built in Come-by-Chance, Newfoundland, and another on a merger between three very tiny exploration, production, and refinery companies based in Wyoming called Tesero Petroleum. Frankly, none of this work heavily influenced me in getting interested in oil and gas. I was actually nearing the end of my second year as a case writer when I was on my way to Los Angeles to write a case on American Cement and I stopped in Palm Springs for the weekend. My father, a commercial banker in Utah, was in Palm Springs attending a mergers and acquisitions seminar. When I arrived to spend the weekend with my parents, Dad told me about a really interesting young guy in our class who was apparently a deep-sea diver. When I heard "deepsea diver," I assumed he was probably a treasure diver and I was really keen to meet him. So, the next day during the seminar coffee break, I introduced myself to Laddie Handleman. Unknown to me at the time, this chance introduction became my introduction to the oilfield service industry. It turns out that Laddie had founded a company called Californian Divers ("Cal Dive"). The company had grown so fast that they were running out of money and were talking about being acquired by Santa Fe. I asked him why he was considering selling and if he had instead thought about raising some venture capital. I told him that I could probably find a few investors who could put enough capital into his company to give it 2 or 3 more years of growth, and then he could sell the business.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».