A Comparative Review of Environmental Policies and Laws Involving Hazardous Private Dams: 'Appropriate' Practice Models for Safe Catchments
Bibliographic record
Abstract
Generally, the world’s largest dams have been erected and managedby governments, while individual owners have been responsible for privatedams. Both kinds of dams have experienced technical failures thathave resulted in tragic losses of life as well as disastrous damage to propertyand environment, and this has generated serious concerns regardingdams’ safety worldwide. In Australia, despite the fact that attention hasbeen focused on the physical and technical integrity of medium- to largescaledams, the smaller private dams have been virtually ignored withregard to their serious potential and actual problems. Specifically, privatedams pose threats to downstream communities and environmentin larger catchments due to these dams having potential cumulativesafety dangers. This paper establishes the significance of this problem.The main issues and concerns surrounding the (lack of) implementationof private dam safety assurance and environmental protection laws havebeen identified and illustrated with Australian case studies. An internationalcomparative review of private dam safety assurance policies, laws,and management practices has been conducted in order to provide a basisfor addressing these issues. The practices analyzed comprise Australia(including New South Wales, Victoria, and Tasmania), the United States(including Michigan and Washington), Canada (including Alberta), theUnited Kingdom, South Africa, and Finland. The review/analysis hasidentified benchmarks for and elements of “best” and “minimum” practicethat can and do exist successfully to control the safety management ofprivate dams and minimize both individual and cumulative dam safetythreats within catchments. These elements have led to the developmentof models of “best” and “minimum” practice and guidelines for selecting“appropriate” practice suitable for varying jurisdictional circumstances;their application is illustrated with an Australian case study. The modelsand associated comparative guidance provided here enable appropriatelaw and policy arrangements for private dam safety assurance to bedetermined and/or checked for any jurisdiction worldwide.
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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".