Challenges for Diadromous Fishes in a Dynamic Global Environment
Bibliographic record
Abstract
Abstract.-Consensus-based research uses scientific inquiry as an unbiased tool to contrast opposing positions in resource conflicts. However, when applied inappropriately, science is more likely to polarize opposing parties. Opposing parties also may use a consensus-based research process to delay actions not in their interest. The recreational smallmouth bass (SMB) Micropterus dolomieu fishery in the St. Croix River, New Brunswick-Maine is an example of how consensus-based research failed to bring opposing sides in a natural resource conflict to a mutually agreeable resolution. Recreational fishing guides blamed high alewife Alosa pseudoharengus returns for the crash of a naturalized SMB fishery in the upper watershed and proposed an exclusion law in response. In 1995, Maine legislated that all fishways in the St. Croix River be closed to alewives, with a resulting decline from 2.6 million alewives in 1987 to 900 by 2002. Claims of insufficient data to describe alewife ecological impacts on SMB led to formation of a local, state, provincial, U.S., and Canadian stakeholders science committee charged with developing a study that would describe SMB and alewife interactions. The committee identified the question "has alewife presence contributed to poor SMB population performance" as of primary importance to moving forward to a resolution. To test this hypothesis, SMB condition and growth, and the diet habits and diet overlap of alewives and SMB, were used as indicators of competition. Results, presented here, indicate that growth did not decline in the presence of alewives, age-0 SMB condition did not decline when alewives were present, and diet overlap between the two species was low in three of four lakes examined. Thus, the available data did not indicate that alewives caused poor SMB population performance. The project results helped break down institutional barriers between Maine natural resource agencies. However, local recreational fishermen were not satisfied with the project process or results, claiming that their interests, as represented by the state freshwater fish and game agency, were not seriously considered. They opposed and nearly defeated a bill to reintroduce alewives to the St. Croix River in 2008, again citing insufficient data to justify the action.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".