A method for deriving net primary productivity and component respiratory fluxes from tower‐based eddy covariance data: a case study using a 17‐year data record from a Douglas‐fir chronosequence
Bibliographic record
Abstract
Abstract Conventional gap‐filling procedures for eddy covariance (EC) data are limited to calculating ecosystem respiration (RE) and gross ecosystem productivity (PG) as well as missing values of net ecosystem productivity (FNEP). We develop additional postprocessing steps that estimate net primary productivity (PN), autotrophic (Ra), and heterotrophic respiration (Rh). This is based on conservation of mass of carbon (C), Monte Carlo (MC) simulation, and three ratios: C use efficiency (CUE, PN to PG), Ra to RE, and FNEP to RE. This procedure, along with the estimation of FNEP, RE, and PG, was applied to a Douglas‐fir dominated chronosequence on Vancouver Island, British Columbia, Canada. The EC data set consists of 17 site years from three sites: initiation (HDF00), pole/sapling (HDF88), and near mature (DF49), with stand ages from 1 to 56 years. Analysis focuses on annual C flux totals and C balance ratios as a function of stand age, assuming a rotation age of 56 years. All six C balance terms generally increased with stand age. Average annual PN by stand was 213, 750, and 1261 g C m−2 yr−1 for HDF00, HDF88, and DF49, respectively. The canopy compensation point, the year when the chronosequence switched from a source to a sink of C, occurred at stand age ca. 20 years. HDF00 and HDF88 were strong and moderate sources (FNEP=−581 and −138 g C m−2 yr−1), respectively, while DF49 was a moderate sink (FNEP=294 g C m−2 yr−1) for C. Differences between sites were greater than interannual variation (IAV) within sites and highlighted the importance of age‐related effects in C cycling. The validity of the approach is discussed using a sensitivity analysis, a comparison with growth and yield estimates from the same chronosequence, and an intercomparison with other chronosequences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".