Net ecosystem productivity of temperate and boreal forests after clearcutting–a Fluxnet-Canada measurement and modelling synthesis
Bibliographic record
Abstract
Clearcutting strongly affects subsequent forest net ecosystem productivity (NEP). Hypotheses for ecological controls on NEP in the ecosystem model ecosys were tested with CO 2 fluxes measured by eddy covariance (EC) in three post-clearcut conifer chronosequences in different ecological zones across Canada. In the model, microbial colonization of postharvest fine and woody debris drove heterotrophic respiration ( R h ), and hence decomposition, microbial growth, N mineralization and asymbiotic N 2 fixation. These processes controlled root N uptake, and thereby CO 2 fixation in regrowing vegetation. Interactions among soil and plant processes allowed the model to simulate hourly CO 2 fluxes and annual NEP within the uncertainty of EC measurements from 2003 to 2007 over forest stands from 1 to 80 yr of age in all three chronosequences without site- or species-specific parameterization. The model was then used to study the impacts of increasing harvest removals on subsequent C stocks at one of the chronosequence sites. Model results indicated that increasing harvest removals would hasten recovery of NEP during the first 30 yr after clearcutting, but would reduce ecosystem C stocks by about 15% of the increased removals at the end of an 80-yr harvest cycle. DOI: 10.1111/j.1600-0889.2010.00500.x
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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 | 0.001 |
| 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) | 0.002 | 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".