Orbital Forcing of Late Miocene–Early Pleistocene Environmental Change in the Zhada Basin, SW Tibetan Plateau
Bibliographic record
Abstract
Abstract Mechanisms controlling the long‐ and short‐term variability of the Indian Summer Monsoon (ISM) and high‐elevation environmental change have largely been examined using low‐elevation or marine records with less emphasis on high‐elevation non‐marine records. We address this using a high‐resolution, long‐term record from upper Miocene–lower Pleistocene (~9.0–2.2 Ma) fluvio‐lacustrine strata in the Zhada Basin, southwestern Tibetan Plateau. Long‐term changes include the onset of lacustrine deposition, a decrease in mean grain size, and an increase in δ18Ocarb and δ13Ccarb values at ~6.0 Ma in response to basin closure following regional extension. This was followed by a return to palustrine/fluvial deposition, an increase in mean grain size, and a decrease in δ18Ocarb and δ13Ccarb values at ~3.5 Ma in response to tectonically driven long‐term ISM weakening. Spectral analysis reveals that high‐frequency variations in the δ18Ocarb record are dominated by 100 and ~20 kyr cycles from ~6.0–2.2 Ma. Wavelet and spectral analysis of the most densely sampled interval (4.23–3.55 Ma), tuned to the record of daily insolation (21 June at 35°N) confirms and highlights 100 and 20 kyr cycles. The tuned Pliocene δ18Ocarb record is coherent with the record of Northern Hemisphere insolation at precession periods but not at obliquity or eccentricity periods. Additionally, the tuned δ18Ocarb record is anticorrelated to the insolation record, indicating that stronger Northern Hemisphere insolation correlates with a stronger ISM. These results suggest that variations in daily insolation drove late Miocene–early Pleistocene high‐frequency ISM variability and environmental changes in the high‐elevation southwestern Tibetan Plateau.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".