Molecular Astrophysics & Astrochemistry
Bibliographic record
Abstract
Many of the facilities and missions that Canada is or will be involved in as well as the diverse science programs that drive these missions rely on the astronomical detection and characterization of atomic and molecular gas as well as dust grains across the electromagnetic spectrum. Molecular bands and dust features have been observed in almost all astrophysical environments—from exoplanet atmospheres to interstellar clouds and star forming regions to AGN winds. Indeed, to date, over 200 molecules and a handful of minerals have been identified in space, and the Universe is aglow with the widespread and abundant emission of Polycyclic Aromatic Hydrocarbons (PAHs). Molecules and dust grains can be powerful probes for the physical conditions in the environment in which they reside, and their presence can help elucidate chemical evolution. However, the vast majority of the known molecular spectral features remain unidentified, and for many identified species, we do not have enough information to turn their spectral appearance into diagnostic probes. Support from laboratory experiments, theoretical calculations and detailed observational data analysis will be crucial to fully exploit astronomical observations in the next decade. This white paper describes the current expertise in Canada in the fields of molecular astrophysics and astrochemistry, detail the key science questions to address within the next decade, and describe the expected needs from laboratory astrophysics and computational chemistry to establish a unique Canadian expertise center in molecular astrophysics and astrochemistry to support upcoming astronomical missions and facilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.034 |
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".