Impact-recovery patterns of water quality in temporary wetlands after fire retardant pollution
Bibliographic record
Abstract
Fire retardants, which are used in wildland fire prevention and extinguishing operations, can cause eutrophi cation of surface waters. We measured water quality in artificially constructed outdoor ponds over three hydrological cycles to determine impact-recovery trajectories in retardant-contaminated, temporary wetlands. We used a multiple beforeafter controlimpact (MBACI) design to determine the effects of application rates that are used in grasslands (1 L·m2) and scrublands (3 L·m2). Retardant application caused a significant increase in the trophic status of the ponds in the postcontamination period (second and third hydrological cycle) relative to the precontamination period (first hydrological cycle). The retardant clearly affected nutrients and indirectly affected chlorophyll a, pH, dissolved oxygen, and Secchi transparency, resulting in a shift from clear water to turbid water stable states. Univariate analyses showed that water quality variables showed distinct recovery trajectories, as influenced by natural, seasonal changes (chiefly water level fluctuations). Nonmetric, multidimensional scaling analyses suggest that water quality did not return to precontamination levels after two hydrological cycles in the retardant-treated ponds. Water quality affected by retardant contamination appeared to maintain wetlands in hysteresis for at least two hydrological cycles and prevented them from returning to the clear water, submerged macrophyte-dominated state.
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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 | 0.001 |
| 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) | 0.000 | 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".