More than 160 near Earth asteroids observed in the EURONEAR network
Bibliographic record
Abstract
Context. The EUROpean Near Earth Asteroid Research (EURONEAR) is a network which envisions to bring some European contributions into the general context traced by the Spaceguard Foundation which was carried out during the last 15 years mainly by the US with some modest European and amateur contributions. Aims. The number of known near Earth asteroids (NEAs) and potentially hazardous asteroids (PHAs) has increased tremendously, mainly thanks to five major surveys all focused on the discovery of new bodies. But also other facilities are required to follow-up and improvement the orbital parameters and to study the physical properties of the known bodies. These goals are better achieved by a co-ordinated network such as EURONEAR. Methods. Astrometry is mandatory in order to acquire the positional information necessary to define and improve orbits of NEAs and PHAs and to study their trajectories through the solar system, especially in the vicinity of Earth. Photometry is required to derive some physical information about NEAs and PHAs. In order to achieve these objectives, the main method of research of the EURONEAR is the follow-up programme of objects selected by a few criteria, carried out mostly at 1 m-class telescopes endowed with medium and large field cameras. Results. 162 NEAs summing more than 1500 individual positions were observed for a total time of 55 nights in both visiting mode and regular runs using nine telescopes located in four countries. The observations were reduced promptly and reported to the Minor Planet Centre (MPC) which validated and included them in the MPC and NEODyS databases following the improvement of their orbital elements. For one binary NEA we acquired photometry and were able to determine its orbital and rotational periods. Complementary to the follow-up work, as many as 500 unknown moving objects consistent with new Main Belt asteroids and one possible NEA were discovered in the analyzed fields. Conclusions. Our positions present 1 precision with an accuracy of 0.2–0.4, sufficient for achieving our immediate main goals. The observations and data reduction were conducted by our network members, which included some students and amateurs supervised by professional astronomers. In most cases, we increased the observational arcs decreasing the uncertainties in the orbits, while in some cases the new positions allowed us to recover some bodies endangered to be lost, defining their orbits.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".