Effects of land use, urbanization, and climate variability on coastal eutrophication in the Baltic Sea
Bibliographic record
Abstract
Sedimentary records of organic matter inputs (stable nitrogen isotopes [δ15N], nitrogen [N], and carbon [C] content), phytoplankton abundance (pigments, stable carbon isotopes [δ13C]), and community composition (pigments) were used to reconstruct the history and pathway to water‐quality degradation in a Swedish Baltic coastal bay. Changes in nutrient sources and cycling began in the 1800s, but eutrophication intensified only after the 1950s, coincident with intensified agricultural practices. Specifically, sedimentary N and C content doubled, δ13C increased, and concentrations of pigments indicative of total algal biomass (ß‐carotene, chlorophyll a), diatoms (fucoxanthin, diatoxanthin), chlorophytes (lutein‐zeaxanthin, Chlorophyll b), and cyanobacteria (canthaxanthin) increased significantly after 1950. Enhanced algal abundance was most strongly related to total N influx and secondly to total P influx. In particular, historical change in algae was related to agricultural N influx and only weakly to agricultural P. In recent decades, wastewater N influx has further stimulated algal biomass, particularly diatoms. In contrast, colonial cyanobacteria were more correlated to total P than total N inputs. Variance‐partitioning analysis explained 60% of the changes in algal abundance and community composition since 1880, with diffuse and point nutrients jointly explaining 36% of the long‐term change in algal biomass. Climate variability has become more important as a factor influencing coastal eutrophication in recent decades, explaining 14% of the variance in the algal data since 1975. Both urban and agricultural sources of nutrients have degraded water quality, illustrating the need for cooperation between stakeholders at regional levels to achieve "good ecological status" in the Baltic coastal environment.
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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".