The surface CO2 gradient and pore-space storage flux in a high-porosity litter layer. Tellus 56B
Bibliographic record
Abstract
We present an hourly time series of the CO2 concentration profile in the top 20 cm of a boreal forest litter layer at a site in northern Manitoba, Canada. The profile data, measured with an automated sampling system during the summer of 1999, show a pronounced daily cycle, with a small surface CO2 gradient and low concentrations during the day and a large surface gradient and high concentrations at night. The CO2 profile measurements allow us to test two current assumptions built into measurements of ecosystem carbon fluxes. The first assumption is that the flux from the surface to the atmosphere can be calculated using the measured CO2 gradient and a calculated value of the diffusive transport coefficient. The behaviour of the surface CO2 gradient suggests that one cannot assume diffusive transport across the moss surface at this site when the friction velocity measured at 30 m exceeds 0.4 m s−1. This condition, associated with turbulent mixing generated by wind shear and/or solar heating of the surface, was often encountered during the day at this site, though rarely at night. During the day, friction velocity and wind speed measured at 30 m height are linearly related, with friction velocity exceeding 0.4 m s−1 when wind speed exceeds about 2 m s−1. At night, wind at the top of the canopy may be laminar, so that the wind speed must exceed 4 m s−1 to cause enough turbulence to raise friction velocity above the 0.4 m s−1 threshold. The second assumption is that changes in soil pore-space CO2 storage can be neglected when correcting eddy covariance measurements for ecosystem respiration that is stored in the ecosystem
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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) | 0.001 | 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".