Improved measurement of radar meteor shower mass indices
Bibliographic record
Abstract
Measurements of meteor radar-derived mass indices are known to be contaminated by sporadic meteoroids Pokorný and Brown (2016). This leads to overestimated values for the mass index and typically inflates associated meteoroid fluxes. Here we apply a novel mixing model approach to retroactively remove sporadic contamination. This technique, previously applied to the Daytime Sextantid meteor shower Kipreos et al. (2022), is adapted to a wider suite of meteor showers. Applying this mixing model to the strongest meteor shower detected by the Canadian Meteor Orbit Radar (CMOR), namely the Geminids, we calculate an uncontaminated differential mass index of 1.51 at a CMOR limiting mass of 10 −7 kg for the stream at its maximum activity. Additionally, we extend this method to eighteen radar-detected showers in total (ARI, BTA, DRA, DSX, ETA, GEM, NIA, NOC, NOO, OCE, ORI, OSE, PCA, QUA, SDA, TCB, XCB, ZPE) and find mass index values ranging from 1.45–1.79, lower than previous estimates. Meteor showers can pose a substantial meteoroid impact risk to spacecraft and astronauts, with mitigation procedures sometimes being required. A shower’s meteoroid risk assessment uses the mass index of that shower, so more accurate shower mass indices, calculated using the mixing model, lead to more accurate meteoroid risk assessments. By utilizing the uncontaminated mass indices of the eighteen most significant meteor showers for satellite impact risk, we re-assess the risk level posed by each, based on the framework from Moorhead et al. (2024a). As a result, the impact risk levels of six showers have been revised downward. Since mitigation procedures require resources such as fuel, time, and operational opportunities, improving impact risk accuracy allows for more efficient mission planning and execution.
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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".