Photosynthesis and physiological traits of evergreen broadleafed saplings during winter under different light environments in a temperate forest
Bibliographic record
Abstract
Photosynthetic traits of the evergreen broadleafed species Camellia japonica L. and Quercus glauca Thunb. were continuously investigated during autumn and winter using saplings that grew in different light environments (gap, deciduous canopy understory, and evergreen canopy understory) in a temperate forest. Light-saturated rates of net photosynthesis in midwinter and spring were lower than those in autumn. Photosynthetic capacity, scaled to a common leaf temperature of 25 °C, increased or remained stable after autumn and then decreased in spring in most leaves. Photosynthetic traits per unit leaf area were different among leaves in different light environments of both Camellia and Quercus during most periods. However, photosynthetic traits per unit leaf mass did not differ among leaves in different light environments, suggesting that differences in photosynthetic traits were mainly due to different leaf mass per area among leaves. Photosynthetic rates under light availability typical in the environment were lower in winter than in autumn in leaves in the sun in a gap but were not different in leaves in the shade under evergreen canopy trees. Thus, the importance of winter carbon gain for annual carbon gain is small in leaves in a gap but is large in leaves under evergreen canopy trees.
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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) | 0.000 | 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 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".