A LAM ICP MS STUDY OF THE DISTRIBUTION OF GOLD IN ARSENOPYRITE FROM THE LODESTAR PROSPECT, NEWFOUNDLAND, CANADA
Bibliographic record
Abstract
L'indice de Lodestar, dans la partie orientale de Terre-Neuve, contient des concentrations importantes d'or dans des breches magmatiques-hydrothermales polymictes mineralisees en sulfures. La teneur maximale, 58.5 g/t d'or, caracterise un echantillon quelconque d'arsenopyrite massive. Les echantillons d'autres mineraux du minerai sulfure, la pyrite, par exemple, ne montrent pas de teneurs comparables. Nous en deduisons que l'or serait directement lie a l'arsenopyrite. Les echantillons contenant des sulfures ont ete etudies avec une batterie de techniques analytiques. Les developpements recents d'analyse in situ par ablation au laser avec plasma a couplage inductif et spectrometrie de masse (LAM-ICP-MS) ont mene a de nouvelles observations a propos de la distribution des elements traces dans les mineraux. Les analyses LAM-ICP-MS ont ete faites pour determiner si l'or est distribue de facon homogene dans les sulfures, en particulier l'arsenopyrite, ou s'il se presente sous forme de micropepites. Les resultats LAM-ICP-MS demontrent que l'indice Lodestar contient des concentrations d'or atteignant 201 g/t. L'or est reparti de facon homogene dans la structure de l'arsenopyrite, sans effet de micropepites. Les autres sulfures, la pyrite et la chalcopyrite, par exemple, contiennent des teneurs en or tres faibles. Les teneurs en or de l'arsenopyrite dans des echantillons individuels varient, probablement en fonction des teneurs en arsenic. Parce que nous n'avons pas pu deceler l'or par les autres techniques micro-analytiques, cet element doit etre incorpore chimiquement dans la structure; on pourrait donc le qualifier d'or invisible.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".