Low moisture availability reduces the positive effect of increased soil temperature on biomass production of white birch ( <i>Betula papyrifera</i> ) seedlings in ambient and elevated carbon dioxide concentration
Bibliographic record
Abstract
White birch ( Betula papyrifera Marsh.) seedlings were grown under two carbon dioxide concentrations ([CO 2 ]) (360 vs 720 μmol mol −1 ), three soil temperatures (T soil ) (5, 15, 25°C initially, increased to 7, 17, 27°C, respectively, one month later), and three moisture regimes (low: 30–40%, intermediate: 45–55%, high: 60–70% field water capacity) for four months in environment‐controlled greenhouses. The dry mass of stem, leaves, and roots was measured after 2 and 4 months of treatment. Low T soil decreased stem, leaf and total biomass in both measurements, however, the decrease was significantly greater in the elevated than ambient [CO 2 ] after 4 months. Intermediate T soil increased root biomass in both measurements. Low moisture reduced stem, leaf, root and total biomass after both 2 and 4 months of treatment. There was a significant T soil ‐moisture interactive effect on leaf, root, and total biomass after 4 months of treatment, suggesting that the magnitude of biomass enhancement in warmer T soil was dependent on the moisture regime. For instance, the increase in total biomass from the low to high T soil was 22, 50, and 47% under the low, intermediate and high moisture regimes, respectively. In contrast, the T soil ×moisture effect on stem biomass was significant after 2 months, but not after 4 months of treatment. High T soil increased leaf mass ratio (LMR) after 4 months of treatment, but decreased both root mass ratio (RMR) after both 2 and 4 months, and root:shoot ratio (RSR) after 4 months of treatment. The low moisture regime decreased LMR after 2 and 4 months of treatment, but increased RSR after 4 months of treatment. There were no significant [CO 2 ] effects on biomass allocation or [CO 2 ]×T soil ×moisture interactions on biomass production/allocation.
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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 | 0.001 |
| 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".