Examining Three‐Dimensional Crustal Heterogeneity in Finland
Bibliographic record
Abstract
The marriage of several high‐quality seismic experiments in Finland over the past 30 years has shown that the saying “something old, something new, something borrowed” can result in the cost‐efficient analysis of large‐scale, three‐dimensional (3‐D) seismic structures. Standing alone, each data set gives a partial view of complex 3‐D structures. When combined, they reveal a 3‐D block structure embedded in a layered crust and enable the analysis of dynamics involved in forming stable cratonic crust. Efforts to collect large 3‐D data sets around the globe include EarthScope (funded by the U.S. National Science Foundation (NSF)), the European Science Foundations (ESF) 4‐D Topography Evolution in Europe: Uplift, Subsidence and Sea Level Change (TOPO‐EUROPE), and the European Space Agency's Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE). Such endeavors are fundamental to modern crustal research. Huge emphasis is placed on collecting and archiving these data, but often only a fraction of data are used in initial studies. Fortunately, new data sets can be complemented with vintage ones (e.g., the NSF‐funded Consortium for Continental Reflection Profiling (COCORP) and ESFs European GeoTraverse (EGT), as well as continent‐wide science programs on continental evolution in Canada (LITHOPROBE), Europe (ESF‐funded EUROPROBE), and the Himalayas (NSF‐funded International Deep Profiling of Tibet and the Himalaya (INDEPTH)). Because fieldwork and archiving have already been completed for these vintage projects, new information can be extracted by new methods, with considerably less effort and funding.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".