Spatial Visualization of Geochemical Data: Application to the Chichinautzin Volcanic Field, Mexico
Bibliographic record
Abstract
The presence of spatial magma heterogeneities in volcanic monogenetic fields is a major observation discussed as well synthesized for worldwide volcanic fields. Magma heterogeneities still have not been visualized in the form of detailed spatial analyst tools, which could further help structuring works of geological mapping, volcanic hazard, and geoheritage evaluations. Here we synthetized 32 published datasets with a novel geochemical mapping model inspired by sub-disciplines of geomatic in one of the most documented monogenetic fields on earth: the Chichinautzin Volcanic Field (CVF) in Mexico. The volcanic units from CVF are covering the 2500 km2 area, and its neighbor stratovolcanoes are bordering the limit of most volcanic centers (Popocatepetl, Iztaccihuatl, and Nevado de Toluca). The results illustrate polygons and point map symbols from geochemical markers such as Alkalis vs SiO2, Sr/Y, and Ba/Nb. The geochemical heterogeneity of the CVF monogenetic bodies decreases as it approaches the Popocatepetl-Iztaccihuatl stratovolcanoes. This alignment is not observed in the occidental CVF portion near the flank of Nevado de Toluca, but geochemical anomalies associated to markers of continental crust interaction such as Sr/Y follow elongated patterns that are not strictly following structural lines and faults mapped on surface.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".