Percent of Medicare Eligible in Alaska enrolled in MA Plans, by County and Type of Plan
Bibliographic record
Abstract
Aleutians East AK Rural 161 0.0% 0.0% 0.0% 0.0% 0.0% Aleutians West AK Rural 203 0.0% 0.0% 0.0% 0.0% 0.0% Anchorage AK Metro 30,910 0.2% 0.2% 0.0% 0.0% 0.2% Bethel AK Rural 1,222 0.0% 0.0% 0.0% 0.0% 0.0% Bristol Bay AK Rural 120 0.0% 0.0% 0.0% 0.0% 0.0% Denali AK Rural 190 0.0% 0.0% 0.0% 0.0% 0.0% Dillingham AK Rural 421 0.0% 0.0% 0.0% 0.0% 0.0% Fairbanks North Star AK Metro 8,917 0.0% 0.0% 0.0% 0.0% 0.0% Haines AK Rural 490 0.0% 0.0% 0.0% 0.0% 0.0% Juneau AK Micro 3,988 0.0% 0.0% 0.0% 0.0% 0.0% Kenai Peninsula AK Rural 9,090 0.0% 0.0% 0.0% 0.0% 0.0% Ketchikan Gateway AK Micro 2,014 0.0% 0.0% 0.0% 0.0% 0.0% Kodiak Island AK Rural 1,326 0.0% 0.0% 0.0% 0.0% 0.0% Lake and Peninsula AK Rural 170 0.0% 0.0% 0.0% 0.0% 0.0% Matanuska-Susitna AK Metro 10,457 0.1% 0.1% 0.0% 0.0% 0.1% Nome AK Rural 744 0.0% 0.0% 0.0% 0.0% 0.0% North Slope AK Rural 461 0.0% 0.0% 0.0% 0.0% 0.0% Northwest Arctic AK Rural 519 0.0% 0.0% 0.0% 0.0% 0.0% Sitka AK Rural 1,186 0.0% 0.0% 0.0% 0.0% 0.0% Southeast Fairbanks AK Rural 1,001 0.0% 0.0% 0.0% 0.0% 0.0% Valdez-Cordova AK Rural 1,209 0.0% 0.0% 0.0% 0.0% 0.0% Wade Hampton AK Rural 491 0.0% 0.0% 0.0% 0.0% 0.0% Yakutat AK Rural 96 0.0% 0.0% 0.0% 0.0% 0.0% Yukon-Koyukuk AK Rural 716 0.0% 0.0% 0.0% 0.0% 0.0% Total** AK 76,102 0.1% 0.1% 0.0% 0.0% 0.1%
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".