Bibliographic record
Abstract
Pressure is a variable that encompasses approximately 12 orders of magnitude (10 -6 to 10 6 atm), and a variety of specialized equipment has been designed over the years to investigate all kinds of materials throughout most of this pressure range.The purpose of this monograph is to expose the reader to a selection of high-pressure studies that have been undertaken recently using various molecular spectroscopy techniques.There are eight chapters in all, the first three of which are focused on applications involving infrared and Raman spectroscopy.Initially, Dr. Yang Song from Western University in Ontario, Canada, describes some of his research on pressure-mediated hydrogen bonding, pressure-induced photochemical reactions, and porous materials and guest-host interactions.Then, Dr. Ian Butler, at McGill University in Quebec, Canada, describes his diamond-anvil vibrational research on solid materials, especially organometallic complexes.Next, Dr. Janice Musfeldt at the University of Tennessee in the USA presents some of her research on molecule-based multiferroics.This chapter is followed by one from Dr. Alexander Goncharov at the Carnegie Institution for Science in Washington, DC, USA, on recent experimental and theoretical investigations of simple molecules at very high pressures approaching and reaching molecular dissociation, and transformations to polymeric and monoatomic structures.In the next chapter, Dr. Helen Walker at the Rutherford
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.422 | 0.265 |
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".