A structured approach to remediation site assessment: lessons from 15 years of fish spawning habitat creation in the St. <scp>Clair‐Detroit</scp> River System
Bibliographic record
Abstract
Ideally, restoration re‐establishes natural processes in degraded habitats (e.g. flow and sediment regimes). However, in altered systems where process‐based restoration is not feasible, habitat construction is another approach to mitigate degradation. Because habitat construction does not directly focus on restoring processes that build and maintain desired habitats, projects must be developed and placed within the contemporary regulatory, ecological, and hydrogeomorphic context of a system, to maximize effectiveness. Here, we develop a framework for evaluating the regulatory, ecological, and hydrogeomorphic components using 15 years of fish spawning habitat construction in the St. Clair‐Detroit River System. The process began by identifying regulatory requirements at a coarse resolution to quickly focus on locations where ecological potential and hydrogeomorphic constraints could be assessed at finer resolutions. Next, ecological potential was assessed using a lithophilic fish spawning habitat suitability index. The suitability index identified five sites for habitat construction and lake sturgeon spawning was documented at each site following construction. However, qualitative monitoring showed fine sediments accumulated at older sites. Thus, geomorphic assessments were incorporated to identify sediment sources and model flow within targeted areas. Since geomorphic assessments required the finest resolution and had the most uncertainty, they were conducted after broad‐scale regulatory considerations and ecological assessments narrowed focus to a few candidate sites. The order of operations identified in this case study evolved from the iterative approach of the restoration team, but in retrospect, it helped develop a framework that directed project development resources to aspects with more uncertainty, where learning is most critical.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".