Comment on "Cosmic-ray-driven reaction and greenhouse effect of halogenated molecules: Culprits for atmospheric ozone depletion and global climate change"
Bibliographic record
Abstract
Lu (2013) (L13) argued that solar effects and anthropogenic halogenated gases can explain most of the observed warming of global mean surface air temperatures since 1850, with virtually no contribution from atmospheric carbon dioxide ( CO2) concentrations. Here we show that this conclusion is based on assumptions about the saturation of the CO2-induced greenhouse effect that have been experimentally falsified. L13 also confuses equilibrium and transient response, and relies on data sources that have been superseeded due to known inaccuracies. Furthermore, the statistical approach of sequential linear regression artificially shifts variance onto the first predictor. L13's artificial choice of regression order and neglect of other relevant data is the fundamental cause of the incorrect main conclusion. Consideration of more modern data and a more parsimonious multiple regression model leads to contradiction with L13's statistical results. Finally, the correlation arguments in L13 are falsified by considering either the more appropriate metric of global heat accumulation, or data on longer timescales.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.027 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".