Far-Field Test of the ICE-4G Model of Global Isostatic Response to Deglaciation Using Empirical and Theoretical Holocene Sea-Level Reconstructions for the Fiji Islands, Southwestern Pacific
Bibliographic record
Abstract
Abstract Holocene paleosea-level data for Fiji, represented by 77 dates and emergence magnitudes, are presented, screened, and adjusted. Most data are from coral microatolls, potentially the most precise paleosea-level indicators in this region. Holocene sea-level changes are reconstructed for five areas within Fiji known to have had different late Quaternary tectonic histories. Resulting analysis suggests that postglacial sea level in Fiji reached its present level more than 6900 14C yr B.P. It also suggests either that a single maximum 5650–3200 14C yr B.P. (perhaps +2.19 m but more likely +1.35–1.50 m) occurred or that two maxima occurred 6100–4550 14C yr B.P. (+0.75–1.85 m) and 3590–2800 14C yr B.P. (+0.90–2.46 m). Broad agreement exists between these empirical sea-level reconstructions and those derived theoretically using the ICE-4G model (predicted maximum ∼4000 14C yr B.P.; ∼+2.1 m). This suggests that both methods of reconstructing Holocene sea-level changes are valid, as are the assumptions underpinning the ICE-4G model. The most important of these, that eustatic sea level had effectively stopped rising by late middle-Holocene time (5000–4000 yr B.P.), is confirmed by observations from Fiji.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".