Assessing Potential Geophysical and Environmental Impacts from Frequent Rocket Launch Missions at Kennedy Space Center
Bibliographic record
Abstract
Kennedy Space Center (KSC) in Florida has been utilized for space missions over many years with a gradually increasing number of rocket launches. There have been multiple studies where air-coupled acoustic waves and infrasound originating from launched rockets were used for operational purposes, such as locating booster trajectories as a function of changingatmospheric conditions. However, no study has utilized the acoustoelastic waves as a signal source for subsurface seismic investigations. We conducted a dispersion analysis using the seismic energy recorded from the Artemis I rocket launch at KSC in November 2022, and from these results we generated depth-sensitivity kernels at different wave frequencies. Thekernels were compared with sedimentary core data to verify boundaries of carbonate layers above the Floridan Aquifer System. Accumulative information of bedrock-sediment boundary across the sedimentary platform could especially provide geo-structural evidence that manifests the configuration of coastal features. Continuous dispersion analysis of seismic recordings from consecutive rocket launches also has potential to identify non-stationary environmental effects,such as reorientation of sedimentary structures and fluctuation of the saltwater/groundwater lens from gravitational tides, which may affect erosional susceptibility of coastal features.
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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 | 0.002 |
| 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 | 0.001 |
| 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".