Notice bibliographique
Résumé
The global mining industry has invested a large amount of capital in mineral exploration and development over the past 15 years in an effort to ensure that sufficient resources are available to meet future increases in demand for minerals. Exploration data have been used to identify specific sites where this investment has led to a significant contribution in global mineral supply of cobalt or where a significant increase in cobalt production capacity is anticipated in the next 5 years. This report provides an overview of the cobalt industry, factors affecting mineral supply, and circumstances surrounding the development, or lack thereof, of key mineral properties with the potential to affect mineral supply. Of the 48 sites with an effective production capacity of at least 1,000 metric tons per year of cobalt considered for this study, 3 producing sites underwent significant expansion during the study period, 10 exploration sites commenced production from 1995 through 2008, and 16 sites were expected to begin production by 2013 if planned development schedules are met. Cobalt supply is influenced by economic, environmental, political, and technological factors affecting exploration for and production of copper, nickel, and other metals as well as factors affecting the cobalt industry. Cobalt-rich nickel laterite deposits were discovered and developed in Australia and the South Pacific and improvements in laterite processing technology took place during the 1990s and early in the first decade of the 21st century when mining of copper-cobalt deposits in Congo (Kinshasa) was restricted because of regional conflict and lack of investment in that country's mining sector. There was also increased exploration for and greater importance placed on cobalt as a byproduct of nickel mining in Australia and Canada. The emergence of China as a major refined cobalt producer and consumer since 2007 has changed the pattern of demand for cobalt, particularly from Africa and Australasia. Chinese companies are increasingly becoming involved in copper and cobalt exploration and mining in Congo (Kinshasa) and Zambia as well as nickel, copper, and other mining in Australia and the South Pacific. Between 2009 and 2013, mines with a cumulative capacity of more than 100,000 metric tons per year of cobalt were proposed to come into production if all sites came into production as scheduled. This additional capacity corresponds to 175 percent of the 2008 global refinery production level. About 45 percent of this cobalt would be from primary nickel deposits, about 32 percent from primary copper deposits, and about 21 percent from primary cobalt deposits. By 2013, about 40 percent of new capacity was expected to come from the African Copperbelt; 38 percent, from Australia and the South Pacific countries of Philippines, Indonesia, New Caledonia, and Papua New Guinea; 11 percent, from other African countries; 5 percent, from North America; and 6 percent, from other areas.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».