Coordinated Population Forecast for Malheur County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066
Bibliographic record
Abstract
During the 2000s, Malheur County, as a whole, experienced population decline (Figure 1); however three of its sub-areas recorded a slight population increase. Adrian grew at an average annual rate of nearly two percent, while Ontario and the area outside UGBs saw more modest growth rates. Even so the population loss recorded by Vale, Nyssa, and Jordan Valley totaled nearly 600, leading the countywide population to decrease. Malheur County’s population decline in the 2000s was the combined result of a diminishing natural increase and periods of substantial net out-migration (Figure 12). The larger number of births relative to deaths has led to a natural increase (more births than deaths) in every year from 2000 to 2015. Net out-migration slowed toward the end of the last decade (2000-2010) combining with a relatively steady natural increase for moderate population increase in four out of the five years since 2010. Malheur County’s total population is forecast to grow by a little more than 400 persons (1.3 percent) from 2016 to 2066, which translates into a total countywide population of nearly 32,000 in 2066 (Figure 1). Population growth is forecast to be modest, becoming increasingly so as time progresses through the forecast period. Forecasting modest population growth is driven by both an aging population—contributing to a steady increase in deaths over the entire forecast period—as well as diminishing net out-migration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".