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Record W2037542249 · doi:10.1029/2007gl029934

Measuring heterogeneous remanence in paleomagnetism

2007· article· en· W2037542249 on OpenAlexafffund
Graham J. Borradaile, Ieva Geneviciene

Bibliographic record

VenueGeophysical Research Letters · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPaleomagnetismRemanenceGeologyGeophysicsRock magnetismSeismologyPhysicsMagnetizationMagnetic field

Abstract

fetched live from OpenAlex

Remanence directions of from the same block‐sample may be inconsistent or unrepresentative due to orientation and location heterogeneity of their remanence‐bearing minerals (RBM). Magnetization‐heterogeneity is usually undetectable at the specimen‐level but we replicated its effects by measuring 8 small specimens with stable magnetizations (8 or 5.2 cm3). These were assembled into a single large multi‐specimen inside 125 cm3 containers that were measured in a Molspin “BigSpin” magnetometer. Large‐specimen remanence directions deflect towards the direction of any strongly magnetized sub‐specimen. Differences between the large‐specimen remanence and that for the group of individually measured sub‐specimens worsened when one sub‐specimen was mis‐oriented. These discrepancies were cancelled or reduced using larger numbers of specimen orientations in the magnetometer. Conventional schemes with 4 or 6 different measurement‐orientations may fail to suppress heterogeneity‐effects whereas our 12‐orientation protocol may succeed. For most specimens, acceptable remanence‐homogeneity is present where similar remanence‐directions are recorded from 4, 6, and 12 different spin‐orientations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.037
GPT teacher head0.299
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes2
Has abstractyes

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