Lasting Impacts of Wildfire Retardant on Phosphorus Storage and Downstream Transport in a Rocky Mountain Stream
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Aerial application of fire retardants is a critical tool in wildland fire suppression but can impact aquatic ecosystems if accidentally misapplied into watercourses. However, exceedingly few studies have documented actual water quality impacts of such misapplications. Here, we explore the short- and longer-term effects of an operational misapplication of PHOS-CHEK LC95A into a Rocky Mountain stream on (a) water quality, (b) streambed storage and release potential of soluble reactive phosphorus (SRP), (c) its downstream transport, and (d) effects on periphyton productivity. Two weeks after the retardant drop, streambed SRP and its potential release into the stream were 2.6–3.0 times greater ( p < 0.014) at the drop site than at a reference site above the drop, yet the greatest increases in aqueous stream SRP occurred 1 km downstream of the drop. One year later, while streambed SRP at the drop site declined by 34%, downstream transport of retardant residues and streambed-bound SRP strongly increased streambed and stream SRP 1.5–6.1 km below the retardant drop. The lasting effects of the retardant observed in this study reinforce the importance of continued refinement of air tanker operational guidelines and enhanced operational attention to protecting critical watersheds during fire suppression operations.
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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 | 0.000 | 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 | 0.000 | 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 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".