Bibliographic record
Abstract
nomic geology) has greatly evolved since its inception in the late 19th century, and has subsequently been strongly influ-enced by mining discoveries. It is a sci-ence that has moved from a descriptive phase to a deeper understanding of ore-body genesis. As a result, deposit types are increasing in number, classification systems are improving, and we are beginning to recognize how spatial and temporal distributions relate to plate tec-tonic mechanisms. Our understanding of ore-forming mechanisms has broad-ened, thanks, in part, to widely available isotopic dating methods and to advances in analytical techniques that determine the ore-element sources, transport con-ditions and depositional processes. It also appears that the climate and funda-mental geodynamic processes (i.e. man-tle plumes) play important roles in ore-deposit formation. SOMMAIRE La science des gîtes minéraux (métal-logénie) s’est beaucoup développée depuis sa création à la fin du XIXème siècle, et a été fortement influencée par les découvertes minières. On est passé de la description à la compréhension des gisements, avec une augmentation du nombre de type, de meilleures classifica-tions et les débuts d’une compréhension des distributions spatiale et temporelle en liaison avec les mécanismes de la tec-tonique des plaques. Les mécanismes de formation ont été mieux compris, en partie grâce aux nombreuses datations isotopiques disponibles, et aux progrès dans l’analyse des processus de source, de transport, et des conditions de dépôt. Le climat et les processus de crises géo-dynamiques (plumes mantelliques) sem-blent jouer un rôle significatif dans la formation des gisements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".