Modeling the influence of temperature on monthly gross primary productivity of sugar maple stands
Bibliographic record
Abstract
A bottom-up and a top-down model were used to estimate the effect of temperature on monthly gross primary productivity (GPP) of sugar maple (Acer saccharum Marsh.). The bottom-up model computed canopy photosynthesis at an hourly time step from detailed physiological sub-models of leaf photosynthesis and stomatal conductance. Leaf mass per area was used as a covariable to integrate photosynthesis through the canopy. The top-down model used a radiation-use efficiency coefficient to relate canopy gross photosynthesis to absorbed photosynthetically active radiation at a monthly time step. The parameters of the top-down model were estimated from simulations with the bottom-up model. Forty single-year simulations were made using records of daily maximum and minimum temperatures from weather stations selected within the natural range of sugar maple in the province of Québec, Canada. Leaf area index was randomly varied between 4 and 10. Within a broad range of values, temperature had a minor effect on predicted monthly canopy-level GPP and its contribution to explaining the variability of GPP was low, both through its direct effect on photosynthetic processes (1.1%), and indirectly through the effect of relative humidity on stomatal conductance (4.0%). This result was unchanged when key parameters relating photosynthesis to temperature and stomatal conductance to atmospheric humidity were changed in the bottom-up model. An increase in time step from hourly to monthly resulted in a downward shift in the optimum temperature range for photosynthesis, from 30 degrees C for a leaf at saturating irradiance to 22 degrees C for the canopy at a monthly time scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".