Automatic and semi-automatic assignment and fitting of spectra with PGOPHER
Bibliographic record
Abstract
Two new tools for computer assisted assignment of rotational spectra with the PGOPHER program are presented, aimed particularly at spectra where many individual lines are resolved. The first tool tries many different assignments, presenting a small number for possible refinement and a preliminary version of this has already been presented. The second tool, the nearest lines plot (a new style of residual plot) provides a clear indication as to whether a trial calculation is plausible, and allows rapid assignment of sets of related lines. It gives good results even for dense spectra with no obvious structure and in the presence of multiple interfering absorptions. The effectiveness of these tools is demonstrated by the analysis of high resolution IR spectra of 8 bands of cis- and trans-1,2-dichloroethene where, including hot bands and isotopologues, 31 vibrational transitions and 158 316 individual lines have been analysed, including perturbations for the higher energy states. Walkthroughs are presented to show this process is rapid.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".