Discussion of “A comparison of local and aggregated climate model outputs with observed data” A black eye for the <i>Hydrological Sciences Journal</i>
Bibliographic record
Abstract
Abstract A paper published by Anagnostopoulos et al. in volume 55 of the Hydrological Sciences Journal (HSJ) concludes that climate models are poor based on temporal correlation between observations and individual simulations. This interpretation hinges on a common misconception, that climate models predict natural climate variability. This discussion underlines fundamental differences between hydrological and climatological models, and hopes to clear misunderstandings regarding the proper use of climate simulations. Citation Huard, D. (2011) A black eye for the Hydrological Sciences Journal. Discussion of ‘A comparison of local and aggregated climate model outputs with observed data’ by G.G. Anagnostopoulos et al. (2010, Hydrol. Sci. J. 55 (7), 1094–1110). Hydrol. Sci. J. 56(7), 1330–1333.
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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 | 0.037 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".