Elemental Analysis of Basalt by Instrumental Neutron Activation Analysis and Inductively Coupled Plasma Mass Spectrometer
Bibliographic record
Abstract
Background: Because of its high sensitivity, activation analysis has become an important tool in a wide variety of science and engineering fields such as industry, mineral exploration, medicine, environmental monitoring and forensic applications.Methodology: Therefore, Instrumental Neutron Activation Analyses (INAA) Au+34 packages from ACT Lab Canada have been used to achieve accurate knowledge about the elemental analysis of basalt rock collected from Hail Northeast of Saudi Arabia.The samples were prepared for irradiation by thermal neutrons using thermal neutron flux of 7×10 12 n cmG 2 secG 1 .Twenty five elements were determined and identified namely: As, Co, Cr, Sb, Sc, Zn, Ba, Br, Sr, Zr, Cs, Hf, Mo, Rb, Th, U and eight rare earth elements namely: La, Ce, Nd, Sm, Eu, Yb, Tb and Lu.In addition, 15 elements were determined and identified by inductively coupled plasma mass spectrometer namely: Be, Cu, Dy, Er, Ga, Ge, Ge, Ho, Nb, Ni, Pb, Sn, Tm, V and Y. Results: The data presented here are our contribution to understanding the elemental composition of basalt rock.Because there are no existing databases for the elemental analysis of basalt, this results are a start to establishing a database for the basalt rock from Hail, Saudi Arabia.Conclusion: The acquired data was serve as a reference for the follow-up studies to assess the agronomic effectiveness of basalt rocks.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".