Paleointensity determination using continuous thermal measurements by a high‐temperature vibrating thermomagnetometer
Bibliographic record
Abstract
A vibrating thermomagnetometer which measures magnetization M continuously at high temperature is used to test a new method of paleointensity determination involving single rather than double heatings. Loss of natural remanent magnetization (NRM) is recorded by the average of 20–25 measurements at the peak temperature T achieved in a zero‐field heating step. Partial thermoremanent magnetization (pTRM) is calculated from the average M after in‐field cooling to room temperature T0. For comparison with data taken at T0, values of M measured at T are multiplied by Ms(T0)/Ms(T), with the aid of the thermomagnetic or Ms(T) curve measured on a chip of the sample. NRM versus pTRM results from 11 heating‐cooling steps on a diabase containing both single‐domain magnetite inclusions in plagioclase and coarser multidomain magnetite grains reproduce the features of Thellier double‐heating paleointensity results for samples from the same site. The NRM – pTRM plot is nonlinear, with convex‐down curvature. For a stringent validation of the single‐heating method, a truly single‐domain sample needs to be tested.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".