Priority water uses from 1998 to 2017 for urban, suburban and rural residents of the Pacific Northwest, USA
Bibliographic record
Abstract
Water is the most important and most debated natural resource in the Pacific Northwest.Researchers, educators and policy makers give a lot of attention to water resources in the region; however, the knowledge of actual water resource priorities of the public is lacking.Consequently, the purpose of this research study was to document how urban, suburban and rural residents living in the Pacific Northwest prioritize water resource issues over the last 20 years.A survey instrument was developed to ask the public about 10 common water uses in the region.This survey was statistically designed using the Dillman methodology and delivered to the public using a mail-based survey process in 1998, 2002, 2005, 2007, 2010, 2012, 2015 and 2017.Water uses in this study were the importance of water for: irrigated agriculture, aquatic habitat, commerce, drinking water, household landscapes, industry, power generation, recreation, snowpack and wetlands.Each water use could be categorized by survey respondents as very important, important, somewhat important, not important or no opinion.In 2017, drinking water, snowpack, wetlands, recreation, aquatic habitat, industry, commerce, power generation, household landscapes and irrigated agriculture were considered an important or very important use of water by over 95, 80, 79, 77, 76, 74, 64, 63, 52 and 47% of the public, respectively.Over time snowpack, recreation, wetlands and aquatic habitat protection have become more important to the public.Conversely, the importance of water for irrigated agriculture, power generation and commerce have become less important to the public.However, at least 45% of all respondents considered all ten questioned water uses important.Survey respondents were placed into three groups (urban, suburban or rural) based on the population of their home county.Rural residents considered the use of water for agriculture more important than both suburban (84 vs. 46%) and urban (84 vs. 35%) residents.Conversely, urban residents were more likely to consider the preservation of wetlands as very important, compared to suburban and rural residents.The demographic factors of gender, education and age also impacted how the public viewed the importance of different water-related issues.Females were more likely than males to consider most water uses important.Respondents with more formal education were more likely to consider snowpack and aquatic habitat important than those with less formal education.Younger respondents were more likely than older residents to consider wetlands and aquatic habitat important.The information from this study will be used to assist policy makers in their decision-making processes about water resource issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".