Quantifying the effect of forest age in annual net forest carbon balance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it