Tribal Perspectives on Preventing the Introduction of Zebra Mussels into Flathead Lake, Montana
Bibliographic record
Abstract
Dreissenid mussels are known to alter ecological processes and thus the provision of ecosystem services. Few studies exist linking changes in ecosystem services from aquatic invasive species (AIS) to changes in human welfare. Preventing AIS introduction is a priority for protecting ecosystem services. However, monetization of AIS disruptions to ecosystem services is rare, producing incomplete cost estimates for AIS-related damages and, by extension, inadequate public policy decisions. Non-Anglo American value systems are also absent from public policy. Including indigenous peoples’ nonmarket values is necessary for a more comprehensive accounting of the distributional consequences of management decisions across affected parties. In co-development with the the Confederated Salish and Kootenai Tribes (CSKT) of the Flathead Reservation (Montana), we have designed a survey instrument and research approach with cultural relevance and methods appropriate to a Tribal setting. Based on a physical/chemical/ecological simulation model of the Flathead Lake ecosystem, we developed a set of scientifically plausible characterizations of ecosystem services affected by dreissenid mussel invasion. We pretested the salience of these hypothetical outcomes in focus groups.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".