Optimal locations of sea-level indicators in glacial isostatic adjustment investigations
Bibliographic record
Abstract
Abstract. Fréchet (sensitivity) kernels are an important tool in glacial isostatic adjustment (GIA) investigations to understand lithospheric thickness, mantle viscosity and ice-load model variations. These parameters influence the interpretation of geologic, geophysical and geodetic data, which contribute to our understanding of global change. Recently, sensitivity kernels have been extended to laterally heterogeneous Earth models using the finite-element formulation, which enabled detailed studies on the sensitivity of the different geodetic observations of GIA such as GPS and terrestrial and space gravimetry. In this study, we discuss global sensitivities of relative sea-level (RSL) data of the last 18 000 yr. This also includes indicative RSL-like data (e.g. lake levels) on the continents far off the coasts. We present detailed sensitivity maps for four parameters important in GIA investigations (ice-load history, lithospheric thickness, background viscosity, lateral viscosity variations) for up to 9 dedicated times. Assuming an accuracy of 2 m of RSL data of all ages, we highlight areas around the world where, if the environmental conditions allowed its deposition and survival until today, RSL data of at least this accuracy may help to quantify the GIA modelling parameters above. The sensitivity to ice-load history variations is the dominating pattern covering in times of 14 ka BP and older almost the whole world. Lithospheric thickness variations are mainly only possible to be determined in certain high-latitude areas around the large former and current ice sheets. Background viscosity as well as lateral viscosity variations can be traced at most coast and shelf areas around the world, especially when dated to be older than 10 ka BP. The latter three are almost everywhere overlapped by the ice-load history pattern. In general we find that the more recent the data are, the smaller is the area of possible RSL locations which could provide enough information on the four GIA modelling parameters. But, we also note that when the accuracy of RSL data can be improved, e.g. from 2 m to 1 m, these areas become larger allowing better inference of background viscosity and lateral heterogeneity. Although the patterns depend on the chosen models and error limit, our results are indicative enough to outline areas where one should look for helpful RSL data of a certain time period.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".