Agricultural and Forest Meteorology 123 (2004) 201–219 Sensitivity and uncertainty of the carbon balance of a Pacific Northwest Douglas-fir forest during an El Niño/La Niña cycle
Bibliographic record
Abstract
The annual net ecosystem productivity (FNEP) of a second-growth Douglas-fir stand established in 1949 on the Canadian West Coast varied considerably over the 4-year period between 1998 and 2001. This period included the El Niño/La Niña cycle during the northern hemispheric winters of 1997/1998 and 1998/1999, offering a unique opportunity to study how a typical forest ecosystem in the Pacific Northwest reacts to interannual climate variability. This was possible even though annual FNEP values calculated from eddy covariance (EC) measurements of CO2 fluxes were subject to biases. These were largely due to the failure of the EC method to accurately measure losses of CO2 under low turbulence conditions at night, which caused FNEP overestimates of as much as 90 g C m−2 per year. As these biases were largely unaffected by interannual climate variability, it was possible to reliably quantify interannual differences in FNEP estimates if they were larger than random variability, which was estimated to be±30 g C m−2 per year at most. Interannual differences were mainly due to differences in ecosystem respiration (R) between the 4 years. In the year following the 1997/1998 El Niño, high air temperatures led to the highest annual R of the 4 years, while annual gross ecosystem photosynthesis (P) was only slightly higher than normal. This resulted in 1998 having the lowest FNEP (270 g C m−2 per year) of the 4 years. For 1999, a cool and cloudy La Niña year, FNEP was
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".