Bibliographic record
Abstract
Eddy covariance (EC) is an important measurement technique used in physical geography and atmospheric sciences to measure the exchange of carbon dioxide between an ecosystem and the atmosphere at a specific location. However, EC produces a net exchange of carbon dioxide yet research questions require an understanding of component fluxes, carbon dioxide uptake by plants through photosynthesis and carbon dioxide emissions due to plant and soil respiration. There are two major methods to partition EC measurements into these component fluxes: night-time and day-time partitioning methods. In the night-time method, nighttime measurements are used to estimate daytime respiration and calculate photosynthesis as a residual and in the daytime method, a light response curve is created to estimate daytime respiration and photosynthesis. This study investigates the benefits and drawbacks of these partitioning methods on two carbon dioxide exchange datasets from ecosystems in Canada. The research sites were a) Mer Bleue, a peatland bog near Ottawa, Ontario and b) Cape Bounty, a high arctic tundra in Nunavut. By using a combination of the REddy-Proc software package, developed by the Max Planck Institute for Biogeochemistry, along with additional Matlab processing, the differences in photosynthesis and respiration due to partitioning methods are presented and discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".