Magnetic properties of sediments of the <scp>R</scp>ed <scp>R</scp>iver: Effect of sorting on the source‐to‐sink pathway and its implications for environmental reconstruction
Bibliographic record
Abstract
Abstract We conducted a mineral magnetic study of river bank and subaqueous delta sediments from the Red River, in order to examine the role of sedimentary sorting on the variation of sedimentary magnetic properties from source to sink. The magnetic mineralogy mainly consists of magnetite and hematite. Bulk sediment particle‐size variations have a strong influence on magnetic properties, with the frequently used magnetic parameters χ fd %, χ ARM , χ ARM /χ, and χ ARM /SIRM exhibiting positive correlations with the <4 µm fraction, while S‐ratios are negatively correlated with this fraction. Compared with river bank sediments and shallow shoreface (<5 m water depth) sediments, sediments from the deeper (>5 m water depth) part of the subaqueous delta have lower χ and SIRM values, a finer ferrimagnetic grain‐size and higher proportions of hematite, consistent with selective loss of coarse ferrimagnetic grains on the source‐to‐sink pathway. We suggest that variations in magnetic properties in response to particle‐size compositions and therefore depositional environment changes should be carefully addressed when magnetic proxies such as χ ARM /SIRM are used in the study of coastal and marine environmental changes (e.g., sea‐level change). In such cases, the combined use of magnetic properties and geochemical indicators, such as Al/Ti ratio, may provide better results for paleoenvironmental reconstruction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".