The orbital distribution of near-Earth objects inside Earth's orbit
Bibliographic record
Abstract
Canada's Near-Earth Object Surveillance Satellite (NEOSSat), set to launch in early 2012, will search for and track Near-Earth Objects (NEOs), tuning its search to best detect objects with semimajor axis a<1.0 AU. In order to construct an optimal pointing strategy for NEOSSat, we needed more detailed information in the a<1.0 AU region than the best current model (Bottke et al., 2002) provides. We present here the NEOSSat-1.0 NEO orbital distribution model with larger statistics that permit finer resolution and less uncertainty, especially in the a<1.0 AU region. We find that Amors =30.1 ± 0.8%, Apollos =63.3 ± 0.4%, Atens =5.0 ± 0.3%, Atiras (0.718<Q<0.983 AU) =1.38 ± 0.04%, and Vatiras (0.307<Q<0.718 AU) =0.22 ± 0.03% of the steady-state NEO population, where Q is the orbit's aphelion distance. Vatiras are a previously undiscussed NEO population clearly defined in our integrations, whose orbits lie completely interior to that of Venus. Our integrations also uncovered the unexpected production of retrograde orbits from main-belt asteroid sources; this retrograde NEA population makes up ≃0.10% of the steady-state NEO population. The relative NEO impact rate onto Mercury, Venus, and Earth, as well as the normalized distribution of impact speeds, was calculated from the NEOSSat-1.0 orbital model under the assumption of a steady-state. The new model predicts a slightly higher Mercury impact flux.
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.
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 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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".