Editorial: Marine and freshwater quality management
Bibliographic record
Abstract
Marine and freshwater are the essential components of the earth's hydrosphere and their quality management has been one of the most critical and overriding challenges for all the involved researchers, engineers and decision makers around the globe. The availability of the world's scarce water resources is increasingly limited due to the worsening pollution problems caused by the release of diverse, large quantities of pollutants from point or non-point sources into rivers, lakes, and oceans. Through the food chain, these pollutants can cause acute or chronic effects on the health of aquatic organisms and human beings. Within a global changing context, more effective quality management of marine and freshwater systems demands continuously improved knowledge and technologies, sound decisions and best practices, and benign legal and socio-economic environments to cope with the situation. This special issue on marine and freshwater quality management contains the selected papers presented during the International Conference on Marine and Freshwater Environments (iMFE2014), which was held in St. John's, Canada, from August 6 to 8, 2014. The conference was organized jointly by the 2014 Atlantic Symposium of the Canadian Association on Water Quality, the 2014 Annual General Meeting and 30th Anniversary Celebration of the Canadian Society for Civil Engineering Newfoundland and Labrador Section, the 2014 Annual Conference of the International Society for Environmental Information Sciences, and the 2nd International Conference of Coastal Biotechnology of the Chinese Society of Marine Biotechnology and Chinese Academy of Sciences. The areas of scientific interest on which over 110 papers and posters were presented in the conference covered an impressively wide range of topics with significant added-value for scientists, engineers, researchers and policy makers in the field. After a rigorous peer-review process, eight papers have been selected for publication in this special issue, addressing the following topics: (1) environmental modeling, risk assessment …
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
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".