An Investigation the Results of ME-MS81D Tests on Clay Lenses of
Bibliographic record
Abstract
One of the most common Analysis Methods of Major and Minor Elements in Rock and Sediment Samples is x -ray Fluorescence (XRF).The Most Important Limitation of This Method Is High Detection Limit In Normal Condition Is about 100 PPM. To Solve This Problem the More Developed Techniques with Lower Detection Limit Should Be Used. We Can Point To ICP -MS And ICP-AES Methods As Some Examples. In Order To Analyze Elements in Clay Lenses of ASMARI Formation Located In the Right Side Base IN SHAHID ABBASPOUR Dam Sampling Was Performed and This Analysis By ALSCHEMEX Laboratory Group Was Send To Canada. In The Used Analysis Method That Is A Combination Method Called ME-MS81D For The First Time 38 Trace Elements Using The ICP-MS And Also 14 Major and Minor Elements As Oxide Using The ICP-AES Analyzed In The Range Of Study . Finally The Results Of These Experiments To Sedimentary Tracing Operations Of SABZAB Spring Located In the Right Side Base Of The Mentioned Dam Were Used And Also The Were Compared With Results Of XRD Experiments By Khuzestan Water & Power Authority And Hydraulic relationship Of Spring With The Dam Reservoir Was Investigated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".