Thellier paleointensity theory and experiments for multidomain grains
Bibliographic record
Abstract
We extend theories of thermoremanent magnetization (TRM) and partial TRM (pTRM) in multidomain (MD) grains to model thermal demagnetization and pTRM acquisition steps in Thellier paleointensity determination. Because of the interleaving of zero‐field and in‐field heating‐cooling steps to increasing temperatures the initial state for any step is complex, and theoretical modeling is more intricate than for pTRM production or thermal demagnetization separately. At low to moderate temperature T , TRM lost exceeds pTRM regained, causing convex down Arai plots sagging below the ideal single‐domain (SD) line. At moderate to high T , pTRM acquisition outweighs TRM loss. As T approaches T Curie , pTRM gain exactly equals TRM loss, and the Arai plot becomes ideal. When pTRMs are produced perpendicular to the original TRM and measured directly rather than by differencing field‐on and field‐off results, there is less deviation from ideality at low to moderate T . Our theory agrees semiquantitatively with results for parallel and perpendicular pTRMs for large MD magnetites (135 μm). Smaller MD magnetites (6 and 20 μm) have less curved Arai plots, and the smallest magnetites (0.6 and 1 μm) have almost linear plots. Positive pTRM checks demonstrate that curved Arai plots of MD grains are reproducible, unlike curved plots for rocks that alter physicochemically in the Thellier experiment, while negative pTRM tail checks indicate undemagnetized pTRM residuals. Low‐temperature demagnetization improves linearity only slightly. Practical applications of this work include using the predicted threshold T below which no net pTRM is produced in a parallel Thellier experiment to screen data used for paleointensity fits. Straight line fits through low‐ and medium‐ T points in Arai plots of MD (135 μm) grains overestimated the paleofield by as much as 100% and for small pseudo‐single‐domain (PSD) (0.6 and 1 μm) grains overestimated by about 25%. However, by using linear segments of medium‐ to high‐ T data with f values ≥0.5 it may be possible to obtain reasonable paleointensity estimates even for larger PSD (6 and 20 μm) and MD grains. Middle‐ to high‐ T fits for 0.6 μm grains gave paleointensities within 4% of the correct value, utilizing essentially the entire data set ( f > 0.9). Perpendicular data always gave superior linear fits. Orienting samples with their natural remanent magnetizations perpendicular to the laboratory field is therefore recommended for rocks containing PSD and MD grains. However, double heatings are preferable to single heatings because they allow pTRM tail checks to be carried out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".