Spatial variability of Holocene relative sea level on the China coast
Bibliographic record
Abstract
<p>The coast of China is located in the far-field of past large ice sheets, and therefore relative sea-level (RSL) data from this region have been used to infer sea-level equivalent changes during the Holocene using geophysical models of the glacial-isostatic adjustment (GIA) process. However, there are known misfits between GIA models and Holocene RSL data along the coast of China. For example, GIA model predictions compared to RSL data from China and the Malay-Thailand peninsula show misfits in the amplitude and timing of maximum RSL (highstand) and temporal variations of RSL from the highstand to the present. Furthermore, two different preferred viscosity profiles were recognized between China and the Malay-Thai Peninsula, suggesting the presence of lateral (3D) variations in mantle viscosity across this region. These previous findings lead to several questions: 1) Are the interpretations of RSL proxies and ages robust? 2) Do 3D GIA models improve the fit compared to 1D models? and 3) Are other local- to regional-scale processes significantly influencing RSL on the China coast?</p><p>We aim to answer these questions by completing an updated, quality-controlled database of Holocene RSL data and integrating it into a spatio-temporal empirical hierarchical model (STEHM) and comparing the results to GIA models. Here, we use standardized protocols to present a preliminary compilation of Holocene RSL data (105 index points and 134 limiting points) from the southern China coast, which extends geographically from 119.19°N (Fuzhou city of Fujian Province) to 111.82°N (Xisha Islands) and 21.82 °E (Qinzhou city of Guanxi Province) to 25.22 °E (Putian city of Fujian Province). Sedimentary (salt marsh, mangrove, tidal flat, lagoon), geomorphic (Chenier ridge, beach rock), fixed biological (oyster), and coral indicators comprise the majority of RSL data in the compilation for the last 12 ka. The database is divided into sub-regions to investigate the influence of tectonics and GIA on RSL.</p><p>We apply the STEHM to the preliminary dataset to estimate the magnitudes and rates of RSL, and compare them to the radially symmetric (1D) ICE-6G_C (VM5a) GIA model pairing and an analysis of the sensitivity of the region to 3D viscosity structure. We find that sub-regional variability (i.e., spatial variability over a shorter spatial scale than the long-wavelength GIA signal) is observed throughout the Holocene. This spatial pattern suggests there is strong influence of local- to regional-scale processes causing RSL to vary from GIA model predictions because no data from sub-regions most sensitive to 3D viscosity structure (e.g., Bohai Bay) were included in these model runs. This preliminary analysis lays the groundwork to assess the potential influence of local-scale processes, such as sediment compaction and tidal range change, as well as regional impacts from tectonic vertical motion.</p>
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".