Hazardous substances in fjords and coastal waters - 2010. Levels, trends and effects. Long-term monitoring of environmental quality in Norwegian coastal waters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Norwegian contribution to OSPAR’s Coordinated Environmental Monitoring Programme (CEMP) includes the monitoring of micropollutants (contaminants) in sediment and marine organisms (blue mussel, snails, prawns, cod, flatfish and deep water fish) along the coast of Norway from the Oslofjord and Hvaler region in the southeast to the Varangerfjord in the northeast. The stations are located both in areas with known or presumed point sources of contaminants, in areas of diffuse load of contamination like city areas, and in more remote areas exposed to presumed low and diffuse pollution. The mussel sites include supplementary stations for the Norwegian Index Programme. The programme includes the monitoring metals, organochlorines, pesticides, dioxins, brominated flame retardants, perfluorinated compounds, as well as biological effects methods. The results from 2011 supplied data to a total of 1035 time series of selected contaminants or biomarkers. Of these, 329 showed statistically significant trends of which 277 were downwards and 52 upwards. The dominance of downward trends indicates that the level of most contaminants is decreasing. Of the 628 median contaminant concentrations assessed in 2011 that could also be classified by Klifs environmental classification system, 78.5% were classified as insignificantly polluted, 16.9% as moderately polluted, 3.5% as markedly polluted (mostly cadmium, lead, chromium, HCB, PAHs), 0.6% as severely polluted (benzo[a]pyrene, carcinogen-PAHs, ppDDE) and 0.5% as extremely polluted (dioxins).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it