Characterization of the Auroral Electrojet and the Ambient and Modified D Region for HAARP Using Long-Path VLF Diagnostics
Bibliographic record
Abstract
During the period of performance a program of instrument construction, technique development, data acquisition, and data analysis was carried out in order to characterize the auroral electrojet and the ambient and modified D-region directly above and near the HAARP (High Frequency Active Auroral Research Program) facility in Alaska. To accomplish this goal, five VLF D-region diagnostic systems were constructed and deployed in five High Schools near the HAARP facility and along the west coast of Alaska. In addition in order to characterize the auroral electrojet on a continental scale and to allow prediction of its location, four VLF D-region diagnostic systems were constructed and deployed in four High Schools on the east coast of the United States and Canada. Data from the complete array of D-region diagnostic systems was acquired during a number of Fall and Spring HAARP campaigns during the period of performance. This data was then used to determine the state of the D-region above HAARP during the campaigns and to provide information on the location of the auroral electrojet. The salient scientific results from the observations were published in the scientific literature and also reported at the annual RF Ionospheric Interactions Workshop in Santa Fe, New Mexico. In addition a portion of this data formed one of the bases of a Ph.D. dissertation completed at Stanford University.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".