Optimal locations of sea-level indicators in glacial isostatic adjustment investigations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. We discuss global sensitivities of relative sea-level (RSL) data of the last 18 000 years. 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 nine dedicated times. Assuming an accuracy of 2 m of RSL data of all ages (based on analysis of currently available data), 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 modeling parameters above. The sensitivity to ice-load history variations is the dominating pattern covering almost the whole world before about 13 ka (calendar years before 1950). The other three parameters show distinct patterns, but are almost everywhere overlapped by the ice-load history pattern. The more recent the data are, the smaller the area of possible RSL locations that could provide enough information to a parameter. Such an area is mainly limited to the area of former glaciation, 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. Our results also indicate that as long as the ice-load history is not sufficiently known, the inference of lateral heterogeneities in mantle viscosity or lithospheric thickness will be interfered by the uncertainty of the ice model.
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.
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) | 0.000 | 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 it