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Record W1584878816 · doi:10.1029/2012gc004213

Diagenetic alteration of magnetic minerals in Labrador Sea sediments (IODP Sites U1305, U1306, and U1307)

2012· article· en· W1584878816 on OpenAlexaboutno aff
Noriko Kawamura, Naoto Ishikawa, Masayuki Torii

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiagenesisGeologyMagnetiteGeochemistrySedimentClay mineralsMineralMineralogyAuthigenicOrganic matterGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

In order to reveal the potential effects of early diagenesis on magnetic minerals in deep‐sea sediments, we studied early diagenetic zones and magnetic mineral characteristics of Lower Pliocene hemipelagic sediment samples from IODP Sites U1305, U1306, and U1307 on Eirik Drift, Labrador Sea. All samples analyzed were unlithified silty clay sediments recovered by a piston corer from depths down to 200 meters composite depth (mcd). Based on shipboard interstitial‐water geochemistry, we divided the sediment column from each site into six early diagenetic zones. Magnetite (Fe 3 O 4 ) was present at all analyzed depths, whereas maghemite ( γ Fe 2 O 3 ) was found only above the iron reduction zone. We attribute this to associated changes in interstitial redox conditions, which induced preferential dissolution of maghemitized surfaces on magnetite grains. Mineral magnetic results indicate a general down‐hole change in mean grain size of magnetic minerals. At Site U1307, which has relatively low organic carbon contents, the diagenetic zones occur at greater depths than at the other studies sites. This suggests that interstitial oxygen levels at this site remained high enough to degrade organic matter through oxic bacterial activity, and that detrital magnetic minerals have been preserved even at depth.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.220
Teacher spread0.211 · 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 teacher head, not a consensus.

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

Citations30
Published2012
Admission routes1
Has abstractyes

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