Drought × <scp>CO</scp><sub>2</sub>interactions in trees: a test of the low‐intercellular<scp>CO</scp><sub>2</sub>concentration (<i>C</i><sub>i</sub>) mechanism
Bibliographic record
Abstract
Models of tree responses to climate typically project that elevated atmospheric CO2 concentration (eCa ) will reduce drought impacts on forests. We tested one of the mechanisms underlying this interaction, the 'low Ci effect', in which stomatal closure in drought conditions reduces the intercellular CO2 concentration (Ci ), resulting in a larger relative enhancement of photosynthesis with eCa , and, consequently, a larger relative biomass response. We grew two Eucalyptus species of contrasting drought tolerance at ambient and elevated Ca for 6-9 months in large pots maintained at 50% (drought) and 100% field capacity. Droughted plants did not have significantly lower Ci than well-watered plants, which we attributed to long-term changes in leaf area. Hence, there should not have been an interaction between eCa and water availability on biomass, and we did not detect one. The xeric species did have higher Ci than the mesic species, indicating lower water-use efficiency, but both species exhibited similar responses of photosynthesis and biomass to eCa , owing to compensatory differences in the photosynthetic response to Ci . Our results demonstrate that long-term acclimation to drought, and coordination among species traits may be important for predicting plant responses to eCa under low water availability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".