Bibliographic record
Abstract
This checklist is intended to help geologists collect or review geological data on mining prospects in a manner that will conform to the increasingly stringent reporting requirements. Survey, assay, and geological data are the key initial inputs required to build a robust computer-based resource model. Once the resource model is built, a geologist reviewing the model should understand the methods and assumptions used in interpolating from the initial data to the gridded resource model. Closer cooperation between project geologists and resource modellers should improve the way data are collected initially as well as identifying biases, weakness and inconsistencies within the resource model. SOMMAIRE Voici une liste de verification a l'intention des geologues qui ont a collecter et analyser les donnees de gisements mineraux, liste qui leur permettra de se conformer aux normes de compte rendu de plus en plus strictes. Les donnees de leves, de teneur et de geologie constituent les elements cles initiaux indispensables pour l'elaboration d'un modele informatise de la ressource fiable. Le modele de ressource retenu doit permettre au geologue de comprendre la methode suivie ainsi que les hypotheses d'interpolation appliquees aux donnees initiales conduisant au modele matriciel de la ressource. Une meilleure collaboration entre les geologues de projet et les modelisateurs de la ressource devrait permettre d'ameliorer la qualite des donnees initiales collectees et de reperer les biais, faiblesses et incongruites du modele de la ressource.
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 | 0.063 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".