INVASIVE SPECIES AND THE DESTRUCTION THEY BRING
Bibliographic record
Abstract
Madam Chair, thank you for inviting me to appear before this subcommittee to discuss the threat of the Asian carp invasion into the Great Lakes. My name is Michael Hansen. I am the chair of the Great Lakes Fishery Commission. I am also a professor of fisheries at the University of Wisconsin at Stevens Point. The Great Lakes are an extremely valuable resource for both the United States and Canada. The Great Lakes ’ commercial, recreational, and tribal fisheries are valued at more than $7 billion annually (ASA 2008). The lakes provide drinking water for 40 million people and are a rich tourist draw. They are a way of life for the people of the region and a healthy, vibrant Great Lakes ecosystem is immeasurable in economic terms alone. The Great Lakes—and the way of life they support—are under assault from invasive species. Invasive species are defined as non-native animals and plants, both aquatic and terrestrial, that enter new environments, become established, and spread. The Great Lakes are “ground zero ” for aquatic invasions. Today, the lakes harbor more than 185 non-native species (Lodge 2007; Mills et al. 1993; Ricciardi 2001; Sturtevant et al. 2010), many of which entered the lakes accidentally. The rate of introduction into the Great Lakes is not slowing, even with the welcomed institution of some invasive species control measures (e.g., ballast water exchange requirements starting as early as 1989). Some estimate that a new invader
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".