Modelling growing season carbon fluxes at a low-center polygon ecosystem in the Mackenzie River Delta
Bibliographic record
Abstract
A temporal upscaling study was conducted to estimate net ecosystem exchange (NEE) of carbon dioxide and net methane exchange (NME) for a low-center polygon (LCP) ecosystem in the Mackenzie River Delta, for each of the 11 growing seasons (2009–2019). We used regression models to create a time series of flux drivers from in situ weather observations (2009–2019) combined with ERA5 reanalysis and satellite data. We then used neural networks that were trained and validated on a single growing season (2017) of eddy covariance data to model NEE and NME over each growing season. The study indicates growing season NEE was negative (net uptake) and NME was positive (net emission) in this LCP ecosystem. Cumulative carbon (C) uptake was estimated to be −46.7 g C m−2 (CI95% ± 45.3) per growing season, with methane emissions offsetting an average 5.6% of carbon dioxide uptake (in g C m−2) per growing season. High air temperatures (>15 °C) reduced daily CO2 uptake and cumulative NEE was positively correlated with mean air growing season temperatures. Cumulative NME was positively correlated with the length of the growing season. Our analysis suggests warmer climate conditions may reduce carbon uptake in this LCP ecosystem.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".