Bibliographic record
Abstract
Inverse thermoremanent magnetization (ITRM) is reversed to the thermoremanent magnetization (TRM) process: ITRM results from warming from low temperatureTin a magnetic field, while TRM results from field cooling from highT. The development of ITRM was studied in magnetites of grain sizes from submicron to 135μm, in pyrrhotites and in hematite crystals. All three minerals acquired ITRM after warming through their magnetic transitions (35 K for pyrrhotite, 120 and 130 K for magnetite, 250 K for hematite). However, when an impacting meteorite's cold interior warms to ambientTin the geomagnetic field, magnetite is the most likely candidate for acquiring ITRM. The magnetite ITRM blocking temperature distribution was determined from 12 neighboring partial ITRMs in nested field‐on warming plus field‐off cooling cycles (300–20 K). The largest partial ITRMs are produced inTintervals around magnetite's Verwey transition (TV= 110–120 K) and isotropic point (TK= 130 K). Both transitions involve large changes in crystalline anisotropy and renucleation of magnetic domains. ITRM is blocked when initially broad domain walls narrow and are pinned by dislocations. ITRM has contrasting properties to TRM, which is mainly due to blocked single‐domain moments. ITRM is strongest for 3‐ to 20‐μm grains, whereas TRM peaks for submicron magnetites. Only 10–20% of ITRM survives low‐temperature demagnetization (LTD) at 77 K or AF demagnetization to 10–15 mT, compared to 30–90% for TRM. ITRM decreases quasi‐linearly withTin thermal demagnetization. The median unblocking temperatureTUBis ≈300°C and 20–25% survives at 550°C. The low‐TUBpart of ITRM could mimic extraterrestrial NRM of lowTUB, cited as evidence of negligible heating of meteorites in their transfer to Earth. The high‐TUBITRM would contaminate paleointensity determinations up to the highestTsteps. The best cure for ITRM contamination is AF or LTD pretreatment.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".