Searching for Wide Binary Brown Dwarfs around Nearby Stars
Bibliographic record
Abstract
We have started a program to search for Brown Dwarf (BD) companions to nearby stars (5 ≤ d ≤ 10 pc) on long period orbits (up to 2000 AU) using wide field optical/infrared imaging. Our goals in surveying a volume-limited sample of stars are: 1) to determine the binary fraction of widely separated BDs and help constrain the BD formation processes, 2) to search for very cold BDs, 3) to provide a better determination of the age and luminosity of a BD sample. Observations are currently conducted at three observatories, CFHT (3.6m), CTIO (4m) and Observatoire du mont Mégantic (1.6m) and should yield a sample of ~100 star systems to a detection limit of J ≈ 20.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".