Quantitative reconstruction of Holocene sediment sources contributing to the central Jiangsu coast, China: New insights into source‐to‐sink processes
Bibliographic record
Abstract
Abstract Coastal deltaic deposits are the primary locations for sediment storage on Earth, and quantifying their source contributions is a critical prerequisite for delineating S2S patterns in marginal seas. In most cases, quantification for the contribution by fine‐grained sediments (i.e. particle size < 63 μm) is considered to be representative to constrain the overall sediment supply. However, this approach may be inappropriate because large differences exist between the two quantities. Here we propose an approach to solve the problem, which is based on the maximum number of tracers from multiple sediment size fractions incorporating the content of all size fractions of sediment. Using this approach, absolute source contributions during the Holocene are reconstructed that provide a first‐order model for the S2S pattern of the central Jiangsu coast, China. The Huanghe River is the strongest driver for the Holocene sedimentation, with a mean contribution of ~72 ± 6% (1417 × 10 8 t). The absolute contributions from the Changjiang and offshore areas were of secondary importance, (i.e. ~17 ± 1% (330 × 10 8 t) and ~11 ± 5% (217 × 10 8 t), respectively). The results show that a large difference between the relative and absolute source contributions and the assumption that the relative contribution represents the absolute contribution is invalid in a coastal setting. The impact of the Huanghe is mainly based on episodic events, such as the event of 1128–1855 AD . The model also reveals that the offshore sediments are as important as the Changjiang sediments for the central Jiangsu coast during the Holocene. Thus, the model provides both the time series and overall quantities of sediment supply during the formation and evolution of the Holocene tidal flats on the Jiangsu coast. Our findings shed new light on quantitative analysis of sediment sources applicable to future S2S studies of marginal seas. © 2020 John Wiley & Sons, Ltd.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".