Night skies data report: Photometric assessment of night sky quality at Katahdin Woods and Waters National Monument
Bibliographic record
Abstract
This report characterizes night sky conditions in Katahdin Woods and Waters National Monument (KAWW) using measurements made in the park unit and models of regional conditions based on satellite data. The National Park Service (NPS) Natural Sounds and Night Skies Division (NSNSD) collected ground-based observations and obtained calibrated night sky imagery to characterize the night sky at two sites—Katahdin Viewpoint and the Overlook—on August 27 and August 28, 2022. NSNSD also used satellite data from 2022 to create a map of predicted night sky conditions in and around the park. The sky overhead remains remarkably dark, with an average zenith brightness of 21.48 mag/arcsec2. We estimate that more than 93% of stars were still visible most of the time, providing one of the best opportunities in the Eastern United States to observe the natural sky. The whole sky over KAWW is just 10%–19% (mean = 15%) brighter than average natural levels, indicating excellent dark sky conditions on average. In KAWW, the average naked eye limiting magnitude (NELM) is 7.05, which indicates an ability to see very faint astronomical features under good seeing conditions and with proper ocular adaptation to darkness. Sky Quality Meter (SQM) measurements average 21.52 mag/arcsec2, indicating that the zenith is darker than we can accurately measure with an SQM. Based on the visibility of astronomical objects, NSNSD classifies the sky as Bortle Class 2: typical truly dark site. As described by Bortle, “Airglow may be weakly apparent along the horizon. M33 is rather easily seen with direct vision. The summer Milky Way is highly structured to the unaided eye, and its brightest parts look like veined marble when viewed with ordinary binoculars.” The main impacts on KAWW’s night sky quality are light domes from Patten, Millinocket, Bangor, and possibly from Quebec, observed along the horizon.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".