The sensitivity of carbon exchanges in Great Plains grasslands to precipitation variability
Bibliographic record
Abstract
Abstract In the Great Plains, grassland carbon dynamics differ across broad gradients of precipitation and temperature, yet finer‐scale variation in these variables may also affect grassland processes. Despite the importance of grasslands, there is little information on how fine‐scale relationships compare between them regionally. We compared grassland C exchanges, energy partitioning and precipitation variability in eight sites in the eastern and western Great Plains using eddy covariance and meteorological data. During our study, both eastern and western grasslands varied between an average net carbon sink and a net source. Eastern grasslands had a moderate vapor pressure deficit (VPD = 0.95 kPa) and high growing season gross primary productivity (GPP = 1010 ± 218 g C m −2 yr −1 ). Western grasslands had a growing season with higher VPD (1.43 kPa) and lower GPP (360 ± 127 g C m −2 yr −1 ). Western grasslands were sensitive to precipitation at daily timescales, whereas eastern grasslands were sensitive at monthly and seasonal timescales. Our results support the expectation that C exchanges in these grasslands differ as a result of varying precipitation regimes. Because eastern grasslands are less influenced by short‐term variability in rainfall than western grasslands, the effects of precipitation change are likely to be more predictable in eastern grasslands because the timescales of variability that must be resolved are relatively longer. We postulate increasing regional heterogeneity in grassland C exchanges in the Great Plains in coming decades.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".