The impacts of shrub abundance on microclimate and decomposition in the Canadian Low Arctic
Bibliographic record
Abstract
Increasing deciduous shrub abundance in the Arctic could alter the biotic and abiotic controls on carbon (C) cycling in these ecosystems.Betula glandulosa (Michx.)leaf litter was decomposed at three sites of differing shrub abundance in the Canadian Low Arctic for one year.Summer and winter microclimate along with soil nutrients were monitored and lab incubations simulated autumn temperatures and leaching conditions.At the high shrub site, warmer winter soil temperatures contrasted with cooler summer temperatures likely due to deeper snow and greater thickness of moss and organic soil layers compared to the other sites.However, surface mass loss was significantly higher at the shrubbier site only after a full year suggesting that microclimate was not the only influencing factor.At all sites, large mass losses (21-26%) occurred between August and May with no significant differences among sites.The laboratory study suggested that much of the mass loss occurred shortly after litterfall in autumn.iii Many people were involved in supporting me through this thesis.I'd like to thank my supervisor, Elyn Humphreys, who I learned so much from, and whose patience and guidance made the entire process enjoyable.Thanks to my Mom, Dad, boyfriend and the rest of my family who offered continuous encouragement.Also thanks to the geography grad student family for all of the wonderful memories.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".