Response of snow albedo to experimental additions of bushfire aerosols and algae
Bibliographic record
Abstract
<!--!introduction!--><b></b> The exposure of New Zealand’s snow and ice fields to significant inputs of aerosols from Australian bushfires makes NZ an ideal site to study albedo's physical and biological controls. Under projected scenarios of increased frequency and severity of Australian droughts and bushfires, snow tainted by Australian aerosols will become increasingly common in NZ. Likewise, snow/ice algae are expected to respond to climate warming by increasing abundance, biomass, and distribution. Because the bushfire season coincides with the melt period, when snow algae undertake vegetative growth with the availability of liquid water, establishing the effect of bushfire aerosols and algae, and their interactions, on the melting of glacial systems is urgently needed. We present albedo measurements from two controlled field experiments. The experiments at Tasman Saddle used treatments applied to plots with a full factorial experimental design to determine the effects of aerosols on albedo and the resulting snow melt. The experiment involved adding dry aerosols at five treatment levels: 0, 12.5%, 25%, 50%, and 100% of the maximum deposited 2019/20 dust concentrations. At Canyon Creek, snow algae sourced from a nearby site was applied as an additional treatment at five levels. The albedo effect of the treatments was measured with a scanning spectroradiometer. We determined snow melt at each plot using small-scale DEMs before and after the dust/algae was applied and measured physical snow properties. This project will use drones and remote sensing to quantify the effects of aerosol deposition and algal growth on glacier mass balance at the catchment scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".