Replication Data for: Measurements of infrared absorption cross-sections for chloroiodomethane and 2-iodopropane from 305 to 340 K
Bibliographic record
Abstract
This dataset includes the infrared absorption cross-section measurements used in 'Measurements of infrared absorption cross-sections for chloroiodomethane and 2-iodopropane from 305 to 340 K' by Ishtiak et al. (submitted, 2025). The dataset includes: (1) absorption cross-sections and transmittance spectra for chloroiodomethane (CH2ICl), labeled as CH2ICl; and (2) absorption cross-sections and transmittance spectra for 2-iodopropane ((CH3)2CHI), labeled as (CH3)2CHI. Six absorption cross-sections are provided for each compound, labeled by the sample temperature (305, 320, and 340 K). Three of these cross-sections, labeled as 'H2OLinesRemoved', have H2O lines removed in the range 1200-1535 cm-1. The removal of H2O lines leads to artefacts in the cross-section, and each such artefact covers, at most, 0.3 cm-1. Transmittance data for all pressure-temperature pairs used to generate each cross-section are provided at 305, 320, and 340 K for both chloroiodomethane and 2-iodopropane. Six transmittance datasets are provided for each compound, labeled as 'Transmittance'. Three of these datasets, labeled by 'H2OLinesRemoved', have H2O lines removed in the range 1200-1535 cm-1. As mentioned earlier, removing the H2O lines results in artefacts in the transmittance. All measurements were made using an ABB Bomem DA8 Fourier Transform InfraRed (FTIR) spectrometer. All cross-section files have an uncertainty at 2 standard deviations. The dataset also includes three density functional theory geometry, three unscaled frequency, and three scaled frequency datasets for each compound. The datasets include all conformers of each molecule. These calculations use four density functionals, B3LYP, ωB97X, B2PLYP, and DSD-BLYP with and without the D3 dispersion correction across three basis sets: def2-SVP, def2-TZVPPD, and def2-QZVPPD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.103 | 0.118 |
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".