Assessing Wintertime Export Fluxes in the Labrador Sea using ��4Th-��8U Disequilibria and a Mechanistic Particle Sinking Model.
Bibliographic record
Abstract
A forensic sampling system was developed for extraction of material from surfaces for off-line analysis by mass spectrometry. The system is composed of a pulsed valve and vacuum aspirated capture with a membrane filter. Particles are deposited on a surface that contains the analyte of interest and the pulsed valve displaces the particle and analyte mixture that is captured on the filter. The material is extracted from the filter and analyzed by mass spectrometry. The sampling system was utilized to capture from the surface of brick, paper, carpet, fabric (90:10 polyester cotton fabric) and glass. Gas chromatography/mass spectrometry (GC-MS) data verified recoveries of caffeine from the surfaces and the detection limit on glass was found to be less than 1 µg. The sampling system was also used to extract ignitable liquids in simulated arson studies. Porous, non-porous and functionalized nanoparticles were used as extraction particles for gasoline and diesel fuels. GC-MS confirmed the presence of ignitable liquid materials and statistical analysis was used to assess components in burned matrices. Using functionalized C18 particles and porous nanoparticles as extraction particles, gasoline traces were identified. The signal intensity was four times as intense when using functionalized C18 particles compared to porous nanoparticles suggesting a more selective capture of specific ignitable liquids with the C18 particles. A portable system was also developed that utilizes inexpensive components. The system utilizes an automobile fuel injection valve and a compressed air canister, and a portable vacuum for sampling. The system is powered by a 12v lead-acid battery. The prototype design was used to extract small molecules from several surfaces at recoveries comparable to the laboratory pulsed valve sampling and performed better than swab based sampling on porous surfaces.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".