Viscous magnetization, archaeology and Bayesian statistics of small samples from Israel and England
Bibliographic record
Abstract
Certain limestones remagnetize viscously and noticeably over archaeological time‐intervals, after their reorientation into monuments. The laboratory demagnetization temperatures (TUB) for the VRM increase with the installation age; with rates of ∼0.07 C0/year for Israel chalk and ∼0.1C0/year for English chalk. The empirical relationship may be used to date enigmatic buildings or geomorphological features (e.g., land slips). Such correlations also give some insight into the viscous remagnetization process over time intervals τ ≤ 4000 years, which are unobtainable in laboratory studies. The TUB‐age relationship for the viscous remagnetization appears to follow a power law, linearized as log10(τ) ≈ b log10 (TUB). Different pelagic limestones follow different curves and, whereas conventional regression estimates the power law exponent b, the small sample size recommends a Bayesian statistical approach. From sites constructed with pelagic chalk from eastern England, precise prior information (b = 0.761) is compared with less precise information for much more ancient sites in northern Israel (b = 0.873). The collective posterior correlation shows a generalized power law exponent b = 0.849. That regression explains 84.9% of the collective variance in age (r2 = 0.849). Of course, site‐specific calibration is required for archaeological age determinations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".