Investigating ophiolite weathering via lithium isotopes in the Indo-Burma range of northeast India
Bibliographic record
Abstract
The weathering of silicate minerals regulates climate on million-year timescales. Some silicate bedrock, particularly ophiolites, are more susceptible to enhanced weathering than other lithologies. Lithium isotopes (δ7Li) are a proxy that can be used to help track weathering processes due to the fractionation of Li during secondary clay mineral formation. Here we present data collected from tributaries that source the Nagaland-Manipur Ophiolite Complexes in northeastern India, which ultimately flow into the Irrawaddy River in Myanmar, to test the hypothesis that the weathering of ultramafic terrains generated by arc-continent collisions can drive rapid atmospheric carbon drawdown. Major cations, anions, trace elements, δ7Li, and clay mineralogical compositions were measured from river water, bedload, and suspended sediment to gain insight into silicate weathering processes in the region. Our results show streams and rivers in this region contain δ7Li values that range from 20.6 to 31.2‰, with the ophiolitic-sourced tributaries being heavier than the global riverine average of ~23‰. This indicates that rivers draining ultramafic lithologies in warm humid climates are experiencing higher degrees of weathering intensity than other drainages comprised of more felsic lithologies. Additionally, XRD results show that most river bedload contains smectite clays, which may help promote organic carbon burial. These data combined highlight new pathways toward understanding silicate weathering as it relates to atmospheric CO2 drawdown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".