Sustainable Water Management and the 2030 Agenda: Comparing Rain Forest Watersheds in Canada and Brazil by Applying an Innovative Sustainability Indicator System
Bibliographic record
Abstract
Watershed management varies greatly across the world. Local conditions are generally dictated by how watershed management is regulated at national, regional, and local scales. Both multisectoral and community-based participatory involvement in watershed management can positively impact the quality and effectiveness of outcomes. This localization can also be vital to the achievement of the UN’s Sustainable Development Goals. In recent years, the term “sustainability” has become overused, has limited quantifiable meaning, and can create “fuzzy” targets. We suggest that an outcome that focuses on “thrivability” is more appropriate; this refers to the ability to not only sustain positive conditions for future generations but to create conditions that allow for all living things (present and future) to have the ability and opportunity to thrive. A thrivability approach aligns with the 2030 Agenda’s ultimate goal: prosperity for all beings on earth. This study uses a thrivability lens to compare two study sites. Primary and secondary data were collected for both the Regional District of Nanaimo (RDN), Canada, and Hydrographic Region VIII (HR-VIII), Brazil, and have been input and analyzed through our Thrivability Appraisal to determine each region’s watershed thrivability score. The Thrivability Appraisal uses seven sustainability principles as the overarching framework. These are then related to four individual subcomponents of watershed health and three common interest tests based on primary environmental perception and secondary technical data as inputs. Assuming the centricity of water for prosperity, the final scoring is a culmination of the 49 total indicators. A comparison is then drawn to the regions’ capacity to achieve the eight targets for UN Sustainable Development Goal (SDG) 6. The outcome illustrates each region’s water management strengths and weaknesses, allowing for lessons to be learned and transferred to other multijurisdictional watersheds.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".