Bibliographic record
Abstract
The optimal fingerprinting methodology of Allen and Tett (Clim Dyn 15:419–434, 1999) was criticized by McKitrick (Clim Dyn 58:405–411, 2022) who argued that it fails to yield unbiased and consistent coefficient estimates, and the associated residual consistency test (RCT) is uninformative regarding the regression model validity. Chen et al. (Clim Dyn 62:1439–1446, 2023) concurred on key points but showed that consistency could be established under certain conditions. I argue herein that they are sufficiently restrictive as to reaffirm the practical invalidity of the Allen and Tett method. Chen et al. also derived an asymptotic distribution of the RCT. Their result implies the critical values used up to now may be incorrect. I propose an alternative fingerprinting method based on the Instrumental Variables procedure with consistent standard errors and I demonstrate its potential in an application to twentieth century temperature data. I find the modeled anthropogenic signal is detected but needs to be scaled down by 35 to 60%, whereas the modeled natural signal needs to be scaled up 2- to fourfold, to reconcile optimally with observations.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".