Climate controls on C<sub>3</sub>vs. C<sub>4</sub>productivity in North American grasslands from carbon isotope composition of soil organic matter
Bibliographic record
Abstract
Abstract We analyzed theδ13C of soil organic matter (SOM) and fine roots from 55 native grassland sites widely distributed across the US and Canadian Great Plains to examine the relative production of C3vs. C4plants (hereafter %C4) at the continental scale. Our climate vs. %C4results agreed well with North American field studies on %C4, but showed bias with respect to %C4from a US vegetation database (statsgo) and weak agreement with a physiologically based prediction that depends on crossover temperature. Although monthly average temperatures have been used in many studies to predict %C4, our analysis shows that high temperatures are better predictors of %C4. In particular, we found that July climate (average of daily high temperature and month's total rainfall) predicted %C4better than other months, seasons or annual averages, suggesting that the outcome of competition between C3and C4plants in North American grasslands was particularly sensitive to climate during this narrow window of time. Rootδ13C increased about 1‰ between the A and B horizon, suggesting that C4roots become relatively more common than C3roots with depth. These differences in depth distribution likely contribute to the isotopic enrichment with depth in SOM where both C3and C4grasses are present.
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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 | 0.000 | 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 | 0.000 |
| 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".