Environmental Review Approaches by Fish and Wildlife Agencies in the United States and Canada
Bibliographic record
Abstract
We surveyed U.S. state and Canadian provincial fish and wildlife agencies regarding their participation and approach to environmental review (i.e., review of project permit applications or proposals for environmental impacts). Most agencies dedicated a personnel unit to environmental review ranging from 1 individual to a staff of 38, and staffs are administered within various divisions or programs. Agencies annually reviewed from 10 to 7,500 projects for environmental impact, and state and provincial agencies spent an average of 3,681 hours (state) and 700 hours (provincial) on projects monthly. An average of 1,760 hours (state) and 390 hours (provincial) was spent annually on proactive measures such as environmental education and land use planning. Most agencies viewed environmental review as very important; however, agencies generally reported limited success in influencing the outcome of reviewed projects, and many identified this as a dissatisfying aspect of the review process. State and provincial agencies have adopted a variety of approaches to accomplish environmental review. Examining the alternative strategies and approaches employed among agencies may add perspective and provide successful models to enhance other agencies' programs. Se realizó un sondeo en las agencias estatales y provinciales de Pesca y Vida Silvestre de los Estados Unidos de Norteamérica y Canadá en cuanto a su participación y enfoque de evaluaciones ambientales (i.e. revisión de los proyectos para solicitar permisos o propuestas de impacto ambiental). La mayoría de las agencias designan unidades de recursos humanos para revisión ambiental que van de 1 individuo hasta 38, y el personal es administrado dentro de varias divisiones o programas. Las agencias, cada año, revisaron entre 10 y 7,500 proyectos de impacto ambiental, y las agencias estatales y provinciales invirtieron mensualmente en los proyectos, en promedio, 3,681 horas (estatales) y 700 horas (provinciales). La media anual en cuanto al tiempo invertido en definir medidas proactivas, como educación ambiental y ordenamiento de uso de suelo, fue de 1,760 horas (en agencias estatales) y 390 horas (en agencias provinciales). La mayoría de las agencias consideran la evaluación ambiental como muy importante; sin embargo, las agencias generalmente reportan un éxito limitado en cuanto a su influencia sobre los resultados de los proyectos revisados y muchos identificaron esto como un aspecto poco satisfactorio del proceso de evaluación ambiental. Las agencias estatales y provinciales han adoptado una variedad de enfoques para llevar a cabo las evaluaciones ambientales. El examen de estrategias y enfoques alternativos que se emplean entre agencias pudiera brindar una mejor perspectiva y proveer modelos exitosos para desarrollar programas en otras agencias.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".