Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest
Bibliographic record
Abstract
Abstract Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0% ± 2.3%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale.
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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 | 0.001 | 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".