{"id":"W4225693625","doi":"10.22215/etd/2021-14867","title":"Non-Cooperative Spacecraft Pose Estimation Using Convolutional Neural Networks","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Spacecraft; Space debris; Convolutional neural network; Aerospace engineering; Computer science; Low earth orbit; Orbit (dynamics); Earth observation; Artificial intelligence; NASA Deep Space Network; Real-time computing; Remote sensing; Computer vision; Satellite; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003395576,0.0007731911,0.0005771624,0.0003208977,0.0002772382,0.0005322435,0.0007692626,0.0006327665,0.001293769],"category_scores_gemma":[0.001180319,0.0005598334,0.0004326158,0.0004926337,0.0003799277,0.0006458979,0.0007481752,0.0009811542,0.0005052015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006028498,"about_ca_system_score_gemma":0.0006746861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02220215,"about_ca_topic_score_gemma":0.02290515,"domain_scores_codex":[0.9997918,0.00002854974,0.000006949234,0.00007512835,0.00004556448,0.00005211986],"domain_scores_gemma":[0.9996186,0.0001613292,0.00005380332,0.00004782528,0.00009429934,0.00002406777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001524396,0.0000690889,0.001414203,0.0000369048,0.00008424673,0.00007277726,0.00005182806,0.8200397,0.006425488,0.001915231,0.00183035,0.1679078],"study_design_scores_gemma":[0.00000209967,0.00001074402,0.0003489808,0.000002422477,0.000003836417,0.000006295915,0.000003257067,0.9982195,0.000512343,0.0007144256,0.0001734964,0.000002597974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1023348,0.001093352,0.8890168,0.0003310919,0.000140939,0.00003531057,0.0002087565,0.001198106,0.00564086],"genre_scores_gemma":[0.9337177,0.0004067346,0.05594348,0.0001114199,0.00009083251,0.00004310536,0.0005489429,0.00006752372,0.009070215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02220215,"threshold_uncertainty_score":0.04414582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007070676045396616,"score_gpt":0.2302440939936962,"score_spread":0.2231734179482996,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}