Contributions of coniferous and broad-leaved species to temperate forest carbon uptake: a bottom-up approach
Bibliographic record
Abstract
Changes in forest species composition could influence ecosystem carbon uptake rates. To understand how species differed in their contributions to canopy photosynthesis, we investigated how the dominant coniferous (eastern hemlock, Tsuga canadensis (L.) Carr.) and broad-leaved (northern red oak, Quercus rubra L.; red maple, Acer rubrum L.) species in a central Massachusetts forest differed in canopy carbon uptake rates. We considered what factors influenced in situ leaf-level photosynthesis and then used a bottom-up summation approach to estimate species-specific total canopy carbon uptake rates. Variation in canopy light strongly influenced leaf-level photosynthetic rates: sunlit leaves had significantly higher rates than shaded leaves, and photosynthesis increased with canopy height. Species also differed in leaf-level photosynthetic rates, with the broad-leaved species having up to twofold higher rates than hemlock. Within hemlock, needles older than 2 years had lower photosynthesis than younger needles. Variation in leaf-level photosynthesis scaled up to influence canopy carbon uptake rates. Red oak consistently had the highest canopy photosynthetic rates, while through the season, hemlock's relative contribution to carbon flux increased and that of red maple decreased. Thus, in such mixed forests, future changes in species composition could have substantial impacts on forest carbon dynamics, particularly if red oak is the primary broad-leaved species to expand at the expense of hemlock.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".