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Record W2030193538 · doi:10.4141/s02-068

Formation of iron nodules in a Hapludult of central Taiwan

2003· article· en· W2030193538 on OpenAlexvenueno aff
Chung-Wen Pai, M. K. Wang, H. C. Chiang, Hen‐Biau King, Jeen Liang Hwong, Hai‐Ping Hu

Bibliographic record

VenueCanadian Journal of Soil Science · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
FundersNational Science Council
KeywordsSaproliteIlliteKaoliniteUltisolPedogenesisClay mineralsWeatheringVermiculiteMineralogyGeologyMatrix (chemical analysis)GeochemistryChemistrySoil waterSoil science

Abstract

fetched live from OpenAlex

This study compared iron nodules with their matrix materials in a Hapludult from central Taiwan. Mineralogy and 14C ages of the iron nodules were compared with their associated matrix materials. The samples were analyzed by X-ray diffraction (XRD) and accelerated mass spectroscopy (AMS). Hydroxy-interlayered vermiculite (HIV) and illite were the major clay minerals in the soil matrix, but illite and kaolinite were dominant in the iron nodules. Clay mineralogy of iron nodules revealed more intense weathering process than the surrounding soil matrix. 14C dates showed that the age of saprolite and iron nodules were similar, and were both much older than those of the soil matrix. It is suggested that iron nodules developed from residuum of the Tertiary shale (saprolite). During pedogenesis, saprolite acted as a nucleus, where Fe coated on its surface was transferred to form iron nodules. Key words: 14C dates, iron nodules, saprolite, soil matrix, Ultisol

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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 designObservational
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

Citations5
Published2003
Admission routes1
Has abstractyes

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