Bibliographic record
Abstract
Abstract With the separation of two of the three stable isotopes of neon by Sir J.J. Thomson in 1913, mass spectrometers have continued to be the instrument of choice for measuring ratios of isotope abundances. The first magnetic sector mass spectrometer specifically dedicated to this task was designed by A.O. Nier in 1947 and featured simultaneous collection of ion currents. Many diverse instruments have evolved from this design. Decades of development of sample preparation techniques have realized isotope ratio data from many elements in solids, liquids, and gases. Coupling of devices such as gas chromatographs, combustion apparati, and laser probes to mass spectrometers in concert with computer control, have realized features such as unattended operation, compound‐specific isotope analyses and isotopic data spatially resolved over distances of a few micrometers on solid surfaces. Today, applications of isotope ratio mass spectrometry (IRMS) embrace many disciplines with topics such as paleodiets, food adulteration, paleoclimatology, migration of birds and animals, pollutant tracing, ore and oil deposits, meteorology, energy expenditure of animals, including astronauts, and the origin of the universe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.035 |
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".