The effect of scale on the relative importance of climatic and biotic variables influencing methane fluxes from an Arctic wet sedge meadow
Bibliographic record
Abstract
Methane fluxes (FCH4) from an Arctic wet sedge meadow at Daring Lake, NT, Canada were examined during the growing seasons of 2008-2017 over several temporal and spatial scales.The largest methane emissions (seasonal averages of 118 -277 mg CH4 m - 2 d -1 ) were recorded at the plot scale mid-summer using manual chamber methods and were associated with wetter locations with more sedges.Plot-scale FCH4 were negligible where shrubby peat soils were raised above the water table.Ecosystem-scale FCH4 measured on a quasi-continuous basis employing an eddy covariance technique were roughly 50% of plot-scale FCH4.Moisture, temperature and vegetation-related variables explained up to 80% of temporal FCH4 variability (p<0.001).Both magnitudes of FCH4 and relationships with driving variables were not consistent between scales and measurement techniques, demonstrating both the importance of scale in deducing all processes influencing FCH4 variability and the difficulties in upscaling FCH4 at this heterogeneous wetland.I would like to thank the many people who supported and guided me throughout my thesis, without whom it surely would not have been possible.I would like to thank my supervisor, Elyn Humphreys, as her knowledge, enthusiasm and strength were both inspiring and contagious throughout the process.I acknowledge her patience and generosity towards all her students, including myself.I was truly blessed to have such a great role model during these last two years of growth and development.I would also like to thank my friends, family and especially my partner, Shane, for relentless encouragement and support.During the field season I had many different field/research assistants and everyone was eager to lend a hand.Thanks to Caitlyn Proctor, Janelle Nitsizo, Shannon Petrie, Liam Case, Zhalanni Drygeese, Jody Zoe and numerous others at the Daring Lake
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".