Stardust Interstellar Preliminary Examination I: Identification of tracks in aerogel
Bibliographic record
Abstract
Abstract Here, we report the identification of 69 tracks in approximately 250 cm2 of aerogel collectors of the Stardust Interstellar Dust Collector. We identified these tracks through Stardust@home, a distributed internet‐based virtual microscope and search engine, in which > 30,000 amateur scientists collectively performed >9 × 107 searches on approximately 106 fields of view. Using calibration images, we measured individual detection efficiency, and found that the individual detection efficiency for tracks > 2.5 μm in diameter was >0.6, and was >0.75 for tracks >3 μm in diameter. Because most fields of view were searched >30 times, these results could be combined to yield a theoretical detection efficiency near unity. The initial expectation was that interstellar dust would be captured at very high speed. The actual tracks discovered in the Stardust collector, however, were due to low‐speed impacts, and were morphologically strongly distinct from the calibration images. As a result, the detection efficiency of these tracks was lower than detection efficiency of calibrations presented in training, testing, and ongoing calibration. Nevertheless, as calibration images based on low‐speed impacts were added later in the project, detection efficiencies for low‐speed tracks rose dramatically. We conclude that a massively distributed, calibrated search, with amateur collaborators, is an effective approach to the challenging problem of identification of tracks of hypervelocity projectiles captured in aerogel.
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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 | 0.001 | 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) | 0.001 | 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".