Assessing the interactions between red snow algae and black carbon from forest fire soot using a field experiment and remote sensing methods in western Canada
Bibliographic record
Abstract
,My thesis investigates the relationship between snow algae and black carbon from forest fire aerosols. In Chapter One, I discuss the motivation and objectives of this thesis, followed by a background on the importance of glaciers in Western Canada, processes of glacier surface darkening, and a review of the ecology of snow algae and forest fire aerosols. In Chapter Two, I describe and analyze the results from a field experiment conducted on Place Glacier, in which I fertilized snow with wood ash. I observed no difference in snowmelt from the treatment, and due to a loss of data from early snowmelt on the last field visit, the wood ash impact on red snow algae growth was inconclusive. At the beginning of the field experiment, I measured snow reflectance, which I then converted to instantaneous radiative forcing. The average instantaneous radiative forcing for the snow algae at Place Glacier on June 3 was 192 ± 12 W m-2 with a maximum of 274 ± 16 W m-2. This radiative forcing translated to a melt potential of up to 27.8 mm w.e. d-1. In Chapter Three, I use remote sensing data from the Harmonized Landsat Sentinel (HLS) project and reanalysis products from MERRA-2 and ERA-5 Land to model the occurrence of red snow algae on glaciers in British Columbia and Alberta for the summer seasons of 2015 to 2023. I modelled the red snow algae using the random forest regressor and XGBoost algorithms, and used the permutation feature importance and SHAP to evaluate the variables’ influence on the model output. The model explained 60% of the variance in the data and the three most important variables were longitude, black carbon and temperature. These results suggest that black carbon may promote red snow algae growth. Finally, in Chapter Four I conclude with the major findings, limitations and recommendations for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".