Comment on “Asynchronous variation in the East Asian winter monsoon during the Holocene” by Xiaojian Zhang, Liya Jin, and Na Li
Bibliographic record
Abstract
Abstract Comparing paleoproxy records throughout China and two climate models simulation results, Zhang et al. (2015) reported asynchronous Holocene spatiotemporal decline of the East Asian winter monsoon. Six sea surface temperature (SST) proxy records from the Atlantic Ocean and northern Indian Ocean and South China Sea were used to validate climate simulations results. However, the referred Mg/Ca SST record from the western Atlantic Ocean is simply nonexistent and the northeast Atlantic Mg/Ca SST data do not reflect winter SST as Zhang et al. (2015) argued to support the driving mechanisms. Furthermore, the western Indian Ocean SSTs data used in Zhang et al. (2015) do not reflect the winter SSTs or regional changes in the Holocene SSTs. Therefore, we question the validity of model simulation results and hence the reliability of conclusions in Zhang et al. (2015).
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".