Bibliographic record
Abstract
A recent publication by the Near-Earth Object (NEOWISE) team (Mainzer et al.) using data from the Wide-field Infrared Survey Explorer compared the spacecraft's detected near-Earth asteroid subpopulation orbital element distributions to those expected from the Bottke et al. NEO orbital model. They found a discrepency between the detected and expected Aten inclination distribution. We show that the more recent NEO orbital distribution model by Greenstreet et al., when biased using the NEOWISE detection biases, gives a better match to the NEOWISE detections for the Aten ( a < 1.0 AU, Q > 0.983 AU) population in semimajor axis ( a ), eccentricity ( e ), and inclination ( i ) than the Bottke et al. model. A Kolmogorov–Smirnov test gives the probability of drawing the NEOWISE detections from the biased Bottke et al. model as not rejectable (at >99% confidence) for the Aten semimajor axis distribution, but is rejectable at such a high level of confidence for the Aten eccentricity and inclination distributions. For all three orbital element distributions, the biased Greenstreet et al. model provides an acceptable match to the NEOWISE Aten detections. The deficiency in the previous model is likely due to the numerical integration's accuracy having broken down in the high-speed regime for planetary encounters near the Sun, an effect which the newer model does not suffer, and thus likely is the model of preference for perihelia q < 1.0 AU.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".