Band composition analysis: a new procedure for deconvolution of the mass spectra of organometallic compounds
Bibliographic record
Abstract
A new chemometric procedure called band composition analysis (BCA) designed for the deconvolution of mass spectra of organometallics is proposed. BCA generates theoretical bands T(i), then combines them to obtain a model band M, which is finally compared with the experimental band E. All of these steps are realized with computer assistance. This modeling yields four parameters characterizing the experimental band: theoretical and model variances s(2) (theor) and s(2) (model), a fit factor alpha and a contribution x(i) from the theoretical band. If s(2) (theor) > 20 the band is deemed complex and needs modeling. The values alpha > 90 indicate that there is good agreement between the experimental and model bands. BCA is particularly effective for the modeling of complex isotopic bands often present in organometallics. Two illustrations of BCA for tetrabutyltin, C(16)H(36)Sn, and 1,1',2,2',3,3'-hexachloroferrocene, C(10)H(4)Cl(6)Fe, are shown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".