Geochemical Modelling in Space: Solar System Beyond Mars – from Asteroid Belt to Kuiper Belt
Bibliographic record
Abstract
Despite the number of studies on the Moon and other celestial bodies as far as Mars, the number of studies conducted on celestial objects beyond Mars, the significantly high expenses and the required time for space explorations escalate the essence of novel methods to manage such limitations. Space geochemical modelling is a powerful tool for supporting cosmochemistry in studying the geochemistry of visited astronomical objects and predicting the geochemical conditions on more obscure planets and moons. Geochemical models can assist in simulating the evolution of the atmosphere, crust, and interior of planets or moons and the geochemical conditions of their surface or subsurface. The presented work focused on studies to provide a vision of what has been done thus far by analysing, categorizing, and providing the critical points of the research’s objectives, the explored geochemical modelling aspects, and the findings. The approach began by generating a list of 180 celestial objects and selecting a comprehensive set of keywords and phrases for identifying relevant studies about geochemical modelling for celestial bodies. Then advanced bibliometric analysis techniques were employed, including a novel approach consisting of combining Bibliometrix and custom-developed data analysis tools to enhance the relevance of identified publications for target keywords, and to identify influential publications, authors, and journals in this field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".