Isotope amount ratio measurements by MC-ICP-MS
Bibliographic record
Abstract
多接收器电感耦合等离子体质谱（MC-ICP-MS）是高精度测定同位素比值最常用的两种技术之一，广泛应用于考古学、物源研究、医学、核科学、法医学、地球科学和环境科学等多个学科[1-9]。与传统的热电离质谱（TIMS）相比，MC-ICP-MS的样品引入方式简单，灵敏度高，并具有测量高电离势元素的能力，但它会产生更大的同位素分馏/质量偏倚[1, 7]。用MC-ICP-MS测得锂的同位素比值与真实值之间的偏差高达25%[10] （而TIMS中Li的偏差仅1-2%[11]）。 ICP-MS中出现同位素分馏现象的原因至今还不完全清楚，但很可能是由于离子通过取样锥时的超音速气流膨胀，以及在截取锥区域的空间电荷效应引起的。这两个过程都有利于将较重的同位素传输到质谱仪中，导致整个质量区间产生不均匀的响应（灵敏度）[12-14]。质量偏倚的大小也随操作条件而变化，例如，样品气流速，以及采样锥和等离子体矩管末端之间的距离，即样品深度等[15]。此外，同位素分馏也随时间而漂移，并受到样品基体的影响。因此，合适的质量偏倚校正是MC-ICP-MS获得精准同位素比值的关键
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".