Probabilistic relations between acid–base chemistry and fish assemblages in streams of the western Adirondack Mountains, New York, USA
Bibliographic record
Abstract
Surface waters across much of New York’s Adirondack Mountains were acidified in the late 20th century but began to recover following the 1990 amendments to the Clean Air Act. Little data, however, are available to characterize biological impacts and predict recovery of fish assemblages in streams of the region. Quantitative fish and chemistry surveys were completed in 47 headwater streams during summer 2014–2016 to develop logistic (probabilistic) models that characterize the status of contemporary fish assemblages and predict how different nitrogen (N) and sulfur (S) deposition loads may affect future fish assemblages. Models for inorganic monomeric aluminum (Ali) and richness ≥1 species and for acid neutralizing capacity (ANC) and total density >400 fish/0.1 ha, total biomass >1500 g/0.1 ha, brook trout (Salvelinus fontinalis) density >0 or >200 fish/0.1 ha, and brook trout biomass >1000 g/0.1 ha were suitable for evaluating community and population responses to changes in acid–base chemistry. Anticipated changes in national (US) secondary standards for atmospheric emissions of nitrogen oxides (NOx) and sulfur oxides (SOx) to achieve target N and S deposition loads will alter acid–base chemistry and the probabilities for observing various levels of fish metrics in streams across the region and elsewhere.
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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 | 0.002 |
| 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 | 0.001 | 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".