Degradation of water quality in Lough Neagh, Northern Ireland, by diffuse nitrogen flux from a phosphorus‐rich catchment
Bibliographic record
Abstract
Annually resolved fossil records of nitrogen (N) inputs (as sedimentary δ15N, N content), aquatic production (δ13C, C content), and algal abundance and gross community composition (pigments, nonsiliceous microfossils) from Lough Neagh, Northern Ireland (NI), were compared with annual records of climatic variability, atmospheric and urban nutrient loading, whole‐catchment nutrient budgets, and limnological monitoring data to identify the unique effects of N on the eutrophication of a phosphorus (P)‐rich lake during ca. 1933‐1995. Cluster analysis revealed two major biostratigraphic zones. Zone I (ca. 1933–1955) was characterized by moderate lake production, as inferred from low concentrations of most fossil pigments and reduced δ15N signatures but elevated δ13C values and chlorophyte microfossil concentrations. In contrast, Zone II (ca. 1955‐1995) exhibited greatly increased contents of 15N, N, C, and algal pigments, combined with strongly reduced δ13C ratios and chlorophyte fossil abundance, a pattern consistent with recent severe eutrophication. Overall, microfossils of diazotrophic cyanobacteria were most abundant during the transition period between zones (ca. 1955‐1964). Regression analysis revealed that past N influx to the lake (as δ15N; r2 = 0.916, p < 0.0001), colonial cyanobacterial abundance(as myxoxanthophyll; r2 = 0.837, p < 0.0001), and total algal standing crops (as b‐carotene; r2 = 0.388, p < 0.0001) were all strongly correlated to agricultural inputs of N to NI farmland, weakly correlated to P inputs to NI farmland (r2 δ15N = 0.503, p < 0.0001; r2cyanobacteria = 0.296, p < 0.0001; r2total algae = 0.046, p < 0.05), and uncorrelated to most measures of climatic variability and atmospheric or urban nutrient inputs. Thus, degradation of water quality during the 20th century resulted from excessive loading of diffuse N to the lake from P‐rich agricultural lands.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".