When do replanted sub-boreal clearcuts become net sinks for CO 2 ?
Bibliographic record
Abstract
After forest harvesting, sites are initially sources of CO2, but eventually become sinks for CO2 after some period of years following reforestation. This period for boreal forests has been assumed to be 10 years, but this has not been validated empirically for most forest types including sub-boreal spruce-dominated forests of central British Columbia, Canada. Therefore, we sought to determine the timing of the source to sink transition for a sub-boreal clearcut. Clearcuts such as the one documented in this study occurring on glaciolacustrine deposits with relatively poor drainage represent about 20% of the 1.5 million ha in the Prince George area. Net ecosystem CO2 exchange (NEE) for a clearcut was measured over four growing seasons in years 5, 6, 8 and 10 after harvest. A Bowen ratio approach in combination with a bottom-up modeled NEE based on ecosystem component CO2-flux measurements was used for years 5 and 6. In years 8 and 10, growing season NEE was measured using an open-path eddy covariance system. A cross comparison of Bowen ratio and eddy covariance systems was performed and measurements agreed relatively well (r 2 = 0.58). The results demonstrated that while this clearcut was still a source for C (NEE of +336 to +487 g C m � 2 ) after 6 years, it was most likely a sink for C between 8 (NEE of � 189 to � 52 g C m � 2 ) and 10 (NEE of � 185 to � 48 g C m � 2 ) years following harvest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".