Towards UNB_TopoDens version 3.0: The first global 3D topographical density model
Bibliographic record
Abstract
<!--!introduction!--> After the creation and publication of the UNB_TopoDens_2v01 laterally varying topographic density model, many research groups and organizations across the world implemented this high-resolution density information for a wide variety of geodetic and geophysical applications. Our most frequently asked question from users is whether there will be a three-dimensional version of the density model. The answer is yes. By combining the depth dependent estimates of density based on material bulk moduli with models of expected geological structures, we believe that the creation of a three-dimensional density model of topography is possible using already existing datasets. In this study, we investigate the depth dependent density variations within the Earth’s topography and assess the validity of using topographical surface densities to provide density estimates of various crustal structures. The goal of UNB_TopoDens 3.0 is to generate a three-dimensional density model of the Earth’s topography, which is important for geoid modelling applications. Future work will focus on refining crustal models and expanding the density model to the Mohorovičić discontinuity.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".