The development of a completely automated oxygen isotope mass spectrometer
Bibliographic record
Abstract
A completely automated mass spectrometer system has been developed to measure the oxygen isotope ratio of carbon dioxide samples. The system has been shown to have a precision of 0.03°/oo, which is comparable to that quoted for any other system in the world. In addition, the facility is capable of analyzing over one hundred samples per day. The system uses an Interdata minicomputer as the primary controller. The minicomputer monitors the quality of analyses, on-line, and thereby insures that all DEL values are measured to at least 0.04°/oo. Host of the sophistication resides in intelligent controllers within the mass spectrometer console. This design gives a technician considerable power when operating the system in a manual mode. The intelligence of the system is contained within hardware circuits, software within the minicomputer and firmware written for a Motorola 6802 microprocessor. A major contribution of this thesis has been the design and installation of an automated mass spectrometer inlet system. A microprocessor based inlet system controller maximizes the throughput of carbon dioxide samples within the inlet system. The inlet system normally contains four different aliquots of carbon dioxide and introduces these samples to the mass spectrometer, in proper sequence, through a single mass spectrometer admittance leak. The system has been used in the analysis of 111 samples of ice taken from the Steele Glacier, Yukon Territory. The samples taken from a vertical borehole, displayed a sawtooth variation of the oxygen isotope ratio with depth. The data have been explained by a physical model described in an appendix to this thesis. If our interpretation is correct, the isotopic variations have recorded at least four surges of the Steele Glacier.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".