Rapid response of stream dissolved phosphorus concentrations to wildfire smoke
Bibliographic record
Abstract
Abstract Wildfires can produce large plumes of smoke that are transported across vast distances, altering nutrient cycling of undisturbed watersheds exposed downwind. To date, wildfire smoke influence on stream biogeochemical signatures remains an important knowledge gap. Here we evaluate the impacts of wildfire smoke on phosphorus (P) biogeochemical cycling in a temperate watershed in the Finger Lakes Region of Central New York located downwind from record setting Canadian forest fires during the summer of 2023. Daily sampling of stream and rainwaters was conducted over the 2 month smoke period, generating a robust geochemical dataset. Stream dissolved P showed high sensitivity to smoke events, attaining concentrations 2–3 × greater than the pre-smoke period. Subsequent rain events after smoke deposition were identified as a potentially important factor in magnitude and timing of dissolved P responses. These findings demonstrate the capacity for wildfire smoke to trigger rapid, observable changes to stream P chemistry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".