Application of the Laurentian Great Lakes ‘Ecosystem Approach’ towards remediation and restoration of the mighty River Ganges, India
Bibliographic record
Abstract
Abstract The majestic River Ganga is a sacred environment which nurtures more than 650 million people in her large watershed. The Ganga has proved resilient despite the multiple, enormous, environmental stressors placed on her. The Laurentian Great Lakes have also faced severe environmental degradation and the lessons learned there over the past 50 years can provide guidance for the remediation and restoration of the Ganga. One of the more important lessons is defining Beneficial Use Impairments to focus remediation efforts in degraded Areas of Concern. This paper provides a case study of one such impairment, Eutrophication or Undesirable Algae, and shows how it can be applied as part of a broader Ecosystem Approach towards the identification and selection of Ganga Areas of Concern. The 10 proposed Ganga Areas of Concern are intended to provide guidance to all stakeholders on how and where to focus remediation efforts on the Ganga, and similar ecosystems throughout the world.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".