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Spatial Variations in Particle Size and Magnetite Concentration on Cedar Beach: Implications for Grain‐Sorting Processes, Western Lake Erie, Canada

2010· article· en· W2084005917 on OpenAlexaffabout
M. T. Cioppa

Bibliographic record

VenueActa Geologica Sinica - English Edition · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSwashGeologyMagnetiteSortingHeavy mineralGrain sizePlageGeochemistryMineralGeomorphologyMineralogyOceanographyProvenanceShorePaleontology

Abstract

fetched live from OpenAlex

Abstract: This study examined spatial variations in the concentration, grain size and heavy mineral assemblages on Cedar Beach (Lake Erie, Canada). Magnetic studies of heavy mineral‐enriched, dark‐reddish sands present on the beach showed that magnetite (~150 μm) is the dominant magnetic mineral. Surficial magnetic susceptibility values defined three zones: a lakeward region close to the water line (Zone 1), the upper swash zone (Zone 2) and the region landwards of the upper swash zone (Zone 3). Zone 2 showed the highest bulk and mass susceptibility (κ, χ) and the highest mass percentage of smaller grain‐size (<250 μm) fractions in the bulk sand sample. Susceptibility (i.e. κ and χ) values decreased and grain size coarsened from Zone 2 lakewards (into Zone 1) and landwards (into Zone 3), and correlated with the distribution of the heavy mineral assemblage, most probably reflecting preferential separation of large, less dense particles by waves and currents both along and across the beach. The eroded western section of Cedar Beach showed much higher concentrations of heavy minerals including magnetite, and finer sand grain sizes than the accreting eastern section, suggesting that magnetic techniques could be used as a rapid, cost‐effective way of examining erosion along sensitive coastline areas.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0010.000

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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 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

Citations15
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueActa Geologica Sinica - English EditionSame topicGeological formations and processesFrench-language works237,207