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Record W7061205005

Observing Ultra-Low Surface Brightness Objects In A Bright Sky Environment

2024· other· en· W7061205005 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueYork University Digital Library (York University) · 2024
Typeother
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSurface brightnessBrightnessLens (geology)Line (geometry)SkySurface (topology)Satellite
DOInot available

Abstract

fetched live from OpenAlex

An array of $\mathrm{n}$ identical lenses operating simultaneously acts as an optical system that has an effective focal ratio that is faster than that of an individual lens by a factor of $\mathrm{\sqrt{n}}$, which enables imaging ultra-low surface brightness objects. Such optical system, when coupled to narrow-band line filters, offers the opportunity to isolate emission from targets while suppressing light from other sources, such as the sky. In this work, we report on the development of a fast focal ratio system consisting of two 400 mm f/2.8 lenses coupled with CCD cameras to observe ultra-low surface brightness objects in the light of H$\mathrm{\alpha}$ and [OI]. The system was used to search for missing gas around a dwarf spheroidal satellite of the Andromeda Galaxy M31 and to locate the transition zones of nebulae. This system was successfully used to observe ultra-low surface brightness objects in the severely light polluted environment of Toronto.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.005
GPT teacher head0.135
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it