Pine Maximum Latewood Density in Semi‐Arid Northern China Records Hydroclimate Rather Than Temperature
Bibliographic record
Abstract
Abstract Long records of tree‐ring maximum latewood density (MXD) measurements in conifers have been successfully employed to reconstruct summer temperature changes globally. Yet, the potential of MXD as a proxy in semi‐arid, low‐latitude regions for reconstructing either temperature or hydroclimate variability remains largely unexplored. Here, we developed a MXD data set of Chinese pine from semi‐arid northern China, and investigated its sensitivity to different climate variables. We found that the annual self‐calibrated Palmer Drought Severity Index from previous August to current July displays the strongest influence on the MXD variation. The entire MXD chronology (covering 1736–2020) is highly consistent with nearby tree‐ring‐based annual precipitation and drought reconstructions at decadal timescales, confirming a temporally stable hydroclimate signal in our MXD record. In particular, the rapid wetting trend during the 2010–2020 period is well captured by the MXD data. This novel study has wide implications for future use of tree‐ring density data to reconstruct past climate changes globally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".