Urban contamination sources reflected in inorganic pollution in urban lake deposits, Bergen, Norway
Bibliographic record
Abstract
The 7000 years transition from a pristine environment towards a modern city has brought a number of chemical changes and effects to urban lake sediments in Bergen. Metals, such as Pb, Hg, Zn, Cu and Fe, display a large anthropogenic influence and reflect historical point sources that existed within the drainage area from approximately AD 1790 until today. The concentration peaks alternate with intervals of lower concentration due to phases of coarser grained sediment input but also periods of potentially reduced metal influx. All discussed elements, except Cd, increase in concentration with decreasing grain size and also correlate with the amount of clay fraction particles. The results emphasize the importance of considering grain size when interpreting sediment chemistry. Correlation with TOC is not apparent in the same extent. The transition from natural to anthropogenically influenced sediments, which is characterised by a sudden increase of several elements, is accompanied by a reduction in Cd, As and Ni concentration. This is interpreted to be the result of hypoxia, changes in pH and reduced erosional input. Factor analysis and the comparison with reference sediments indicate that the elements Pb, Hg, Zn and Cu most clearly demonstrate man-made pollution. Analyses of stormwater culvert sediments suggest that urban runoff contributes to the pollution load today, with standing building mass and traffic contributing to the load.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".