Igneous Rock Associations 22. Experimental Petrology: Methods, Examples, and Applications
Bibliographic record
Abstract
Experiments are an important source of basic information in petrology, from thermodynamic data used to develop predictive models to physical property data used to understand magma ascent and eruption. Since we all use experimental data in our work as geoscientists, it is important that we have a basic understanding of the methods used to prepare and perform experiments on rocks and minerals and their synthetic analogues. In this review I examine how the observational science of geology changed in the late 1800’s with the development of the interdisciplinary science of physical chemistry. The second part of the paper discusses what factors need to be considered in designing an experimental study; it focuses particularly on the problems of reaching equilibrium on the short timescales available in the laboratory. In the final section, I give four examples of geological problems that have been solved using experimental methods and make some suggestions about the directions that future experimental campaigns might take.RÉSUMÉL’expérimentation est une source importante d’information de base en pétrologie, qu’il s’agisse de données thermodynamiques pour développer des modèles prédictifs, ou des propriétés physiques utilisés pour comprendre la montée et l’éruption d’un magma. Puisque nous utilisons tous des données expérimentales dans notre travail de géoscientifique, il est important que nous ayons une compréhension minimale des méthodes utilisées pour préparer et réaliser des expériences sur les roches, les minéraux et leurs analogues de synthèse. Dans la présente étude, je me suis penché sur les changements survenus en science d’observation qu’est la géologie, à la fin des années 1800, avec le développement de la science interdisciplinaire de la chimie physique. La deuxième partie de l’article traite des facteurs à prendre en compte dans la conception d’une étude expérimentale; elle porte en particulier sur les problèmes d’atteinte d’un équilibre sur les temps courts du laboratoire. Dans la dernière section, je donne quatre exemples de problèmes géologiques qui ont été résolus à l’aide de méthodes expérimentales, et je fais des suggestions sur des orientations qui pourraient être adoptées lors de campagnes expérimentales à venir.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".