Tephra from Ice—A Simple Method to Routinely Mount, Polish, and Quantitatively Analyze Sparse Fine Particles
Bibliographic record
Abstract
A method involving a graphite substrate has been developed for the mounting and analysis of sparse, fine particles from a liquid suspension to enable improved study of volcanic ash (tephra) and atmospheric dust preserved in glacial ice. Unpolished grains may be studied by scanning electron microscope-energy dispersive spectrometry (SEM-EDS) at full vacuum without the need for a conductive coating due to the close proximity of the underlying graphite. The same grains in the same relative positions may be subsequently examined in polished mounts by a variety of methods including SEM-EDS, electron probe microanalysis, laser ablation-inductively coupled plasma-mass spectroscopy, secondary ion mass spectrometry, and optical microscopy. Particles as small as 3-5 microm may be routinely and easily prepared for analysis as polished grains, and particles of significantly different sizes may be exposed simultaneously. The general approach also offers significant flexibility, including both single- and multiple-sample mounts, and may be adjusted to suit a variety of samples and analytical methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".