ALTIN CEVHERLERİNİN KARAKTERİZASYONUNDA KULLANILAN MİKRO ANALİTİK YÖNTEMLER
Bibliographic record
Abstract
Mikro analitik teknikler, cevher karakterizasyon çalışmalarında yaygın bir şekilde kullanılmaktadır.Altın cevherlerinin, özellikle refrakter altın cevherlerinin karakterizasyonunda da etkin bir şekildekullanılan bu teknikler, doğruluk ve hassasiyeti yüksek oldukça faydalı bilgiler sunmaktadır. Cevherhakkındaki bu bilgiler doğru proses seçiminin yapılmasına ya da mevcut prosesin etkin bir şekildekontrol edilmesine olanak sağlamaktadır. Bu çalışmada, altın cevherlerinin karakterizasyonunda,mikroskobik (visible) altının belirlenmesinde kullanılan (QEMSCAN) (Quantitative Evaluationof Mineralogy by Scanning Electron Microscope), MLA (Mineral Liberation Analyzer) gibi SEM(Scanning Electron Microscopy) temelli geliştirilmiş modern otomatik analiz yöntemlerinin yanısıra refrakter altın cevherlerinde mikroskop altında kolayca görülemeyen, çok ince ‘invisible’altının belirlenmesinde kullanılan EPMA (Electron-Probe Micro-Analysis), μ-PIXE (Microparticle-induced X-ray emission) ve SIMS (Secondary-Ion Mass Spectrometry) gibi yaygınolarak kullanılan mikro analitik yöntemler tanıtılmakta ve yapılan güncel çalışmalardan örneklersunulmaktadır.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; both teacher heads agree on what is shown here.
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".