The Development and Historic Use of Habitat Structures in Channel Restoration in the United States: The Grand Experiment in Fisheries Management
Bibliographic record
Abstract
The use of instream structures to modify aquatic habitat has a long history in the United States. Pioneering work by wealthy landowners in the Catskills region of New York produced a range of designs in the decades preceding the Great Depression in an effort to replenish fish populations depleted from overfishing. The scientific evaluation of structures began in 1930. Within two years, a Michigan research team claimed improved fish populations. Cheap labor and government-sponsored conservation projects spearheaded by the Civilian Conservation Corps allowed the widespread adoption of the techniques in the 1930s, before adequate testing of the long-term impact of the devices. The start of World War II temporarily ended the government conservation efforts and prevented the continued evaluation of structures. During the 1940s, 1950s and 1960s, designs of instream structures remained essentially unchanged. Meanwhile, the small number of evaluations of the impact of the structures often were flawed. The continued use of early designs of instream structures helped instill a false belief that instream structures were proven to be a benefit to fish. Even modern use of instream structures continues to rely on the basic blueprints developed in the Catskills, despite documented problems with the use of these designs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".