Large dam renewals and removals—Part 1: Building a science framework to support a decision‐making process
Bibliographic record
Abstract
Abstract Altered rivers and managed flows are a hallmark of civilization and dams are a principal agent of alteration. Peak dam construction occurred at the turn of the last century in Western countries, and many of the largest dams are reaching the end of their service life. As a result, dam operators are increasingly facing a complex renewal/removal decision‐making process in a large part because the economic and social values of dams have changed. The Mactaquac Hydroelectric Generating Station (New Brunswick, Canada), operated by NB Power Corporation (NB Power), is Canada's 25th largest dam and it is reaching the end of its service life. A decision is required for the dam's future state and three options were originally proposed: renew, rebuild, or remove. An overarching science framework was developed with NB Power to inform and support decision‐making for the dam's decision process and an impending Environmental Impact Assessment. The framework guides research and monitoring for dam renewal/removal using science‐based solutions that aim to minimize impacts on the aquatic environment while supporting an efficient and cost‐effective decision‐making process. The framework has five components: (a) establish long‐term baselines of environmental conditions; (b) develop normal ranges describing the river's natural variability; (c) integrated physical and biological modelling; (d) assess the specific and cumulative state of fish passage; and (e) create and sustain a user‐friendly geospatial data management system. In this paper we present a case study that implements the science framework (Part 1) through the Mactaquac Aquatic Ecosystem Study (MAES) with a view to revisit and assess its final impact post‐project completion (Part 2).
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".