<scp>GGR</scp>Biennial Critical Review: Analytical Developments Since 2010
Bibliographic record
Abstract
Advances in the chemical and isotopic characterisation of geological and environmental materials can often be ascribed to technological improvements in analytical hardware. Equally, the creation of novel methods of data acquisition and interpretation, including access to better reference materials, can also be crucial components enabling important breakthroughs. This biennial review highlights key advances in either instrumentation or data acquisition and treatment, which have appeared since J anuary 2010. This review is based on the assessments by scientists prominent in each of the given analytical fields; it is not intended as an exhaustive summary, but rather provides insight from experts of the most significant advances and trends in their given field of expertise. In contrast to earlier reviews, this presentation has been formulated into a unified work, providing a single source covering a broad spectrum of geoanalytical techniques. Additionally, some themes that were not previously emphasised, in particular thermal ionisation mass spectrometry, accelerator‐based methods and vibrational spectroscopy, are also presented in detail.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".