Eddy covariance based surface‐atmosphere exchange and crop coefficient determination in a mountainous peatland
Bibliographic record
Abstract
Abstract In the soil–plant–atmosphere continuum, fluxes of water, energy, and carbon determine the water and carbon balance of peat bogs. We used eddy covariance (EC) measurements to study surface atmosphere exchange and its drivers above an ombrotrophic peat bog (Odersprungmoor) in the Harz Mountains, Germany, with nonideal measurement conditions during the growing season in 2013. For montane peatlands, only very few EC flux measurements exist due to site constraints, for example, surface slope, limited fetch, and frequent dew formation on open path sensors. The measured data were carefully filtered resulting in valid and representative fluxes for the bog. The evapotranspiration (ET) was further characterized by determining the adjusted crop coefficient (Kc*) for July and August and comparing it with Kc* values from 7 years of the FLUXNET site Mer Bleue bog, Ontario, Canada. While soil moisture was taken into consideration, the adjustment was nevertheless necessary as plant health and nutrient supply were not evaluated as required by FAO guidelines. Actual ET at OM was well described by the Kc* model (Kc* = 0.85, R2 = 0.85). The primary control on ET was available energy and atmospheric conditions and, marginally, the soil moisture conditions. This Kc* value is comparable to the calculated Kc* values for MB, which ranged between 0.82 and 0.86 (R2 between 0.84 and 0.97). Since these Kc* ranges are narrow for the different sites and years, we hypothesize that these values are good estimates for the true crop coefficients of Sphagnum‐dominated peat bogs.
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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 | 0.000 | 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) | 0.000 | 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".