Factors Controlling Photomineralization of Organic Carbon in Canadian High Arctic Soils
Bibliographic record
Abstract
Arctic ecosystems are important to consider in the discussion of climate change. The region stores vast amounts of carbon, a large portion of which is situated near the surface. Permafrost thawing due to climate warming can increase the amount of available soil carbon for mineralization to CO2, potentially accelerating climate warming. This mineralization may be carried out by soil microbes through microbial respiration, or through photooxidation of organic carbon. Field measurements recorded in 2024 at the Cape Bounty Arctic Watershed Observatory in Nunavut displayed higher rates of CO2 production in light conditions compared to dark conditions (respiration only), suggesting another process contributing to soil CO2 production. Options include 1) increased temperature during field measurements stimulates microbial activity, and 2) photomineralization of organic matter due to UV light exposure. To explore the relative importance of these processes, I used both field and laboratory methods. In the field, CO2 fluxes were measured using closed chambers and an infrared gas analyzer to measure CO2 fluxes. Soil samples were collected from select field sites that demonstrated this enhanced CO2 production. To explore these relationships in a more controlled environment, soil samples were incubated in the lab and subjected to one of four treatments: 1) The control group, where the sample was incubated at 4°C 2) Exposure to artificial light (visible light) with an intensity of 300 micromoles 3) Warming to 20 degrees and 4) Exposure to Ultraviolet light with UVA and UVB wavelengths using a heating lamp. For each of these experiments, I recorded CO2 production with a PPSystems infrared gas analyzer. The recorded flux measurements will be analyzed to determine how these treatments impact soil CO2 production. The results of this research will inform how photomineralization of bioavailable soil carbon could impact future rates of warming in the Arctic and globally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".