The Effects of Predicted Soil Moisture and Temperature Increase on CO2 Exchange within a High Arctic Ecosystem
Bibliographic record
Abstract
With 2014 being the warmest year on record and 10 of the warmest years occurring after 1997, it is essential to understand the effects of this warming on CO2 exchange. It was also discovered that much of this warming is focused in the Arctic regions, which are sensitive to changes in temperature (Cole & McCarthy, 2015). My research examines the effects of enhanced snowfall and soil temperature on the exchange of CO2 between the land and the atmosphere in a high arctic environment. The research is taking place at Cape Bounty Arctic Watershed Observatory (CBAWO) on Melville Island, Nunavut as part of the International Tundra Experiment (ITEX). The goal of ITEX is to better understand the effects of increased summer temperature and increased snowfall on arctic ecosystems. This is a full factorial experiment including treatments varying precipitation (and likely soil moisture), soil temperature, moisture and temperature together, and a control that is at ambient soil moisture and temperature. Snow fences are used to enhance precipitation, while open-topped transparent chambers are used to increase soil temperature. In a companion lab experiment, I look at the effects of different soil moisture levels and temperatures on soil CO2 production in a more controlled environment. Two temperatures, two moisture levels, and eight replicates of each will be established in sealed incubation chambers, and soils will be incubated for 33 days. Presently a significant relationship has been found between soil moisture and CO2 flux within the field experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".