A comparison of gap-filling methods for a long-term eddy covariance dataset from a Northern Old-growth Black Spruce forest
Bibliographic record
Abstract
Boreal old-growth forests are key determinants in the global carbon cycle. It is unknown how the role of persistent old-growth forests will be in the carbon cycle in the face of predicted climatic changes. Eddy-covariance measurements are commonly used to quantify carbon exchange between ecosystems, such as forests, and the atmosphere. Error due to gap-fill method is of particular interest in these datasets. Here we filled a 15-year eddy covariance dataset from the Northern Old-Growth Boreal Black Spruce (Picea mariana) site located near Thompson, in central Manitoba, Canada using four different gap-fill methods. Our objectives were to determine if choice of gap-fill method affected annual NEP and if these errors compounded to even greater differences over the 15-year study period. Most significant differences in NEP among methods occurred from September to December, but variations during the growing season were responsible for most of the annual differences.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".