Spatial Variations in Particle Size and Magnetite Concentration on Cedar Beach: Implications for Grain‐Sorting Processes, Western Lake Erie, Canada
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".