{"id":"W2555984598","doi":"10.1155/2016/3518537","title":"A GRASP for Next Generation Sapphire Image Acquisition Scheduling","year":2016,"lang":"en","type":"article","venue":"International Journal of Aerospace Engineering","topic":"Satellite Communication Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Science Basic Research Program of Shaanxi Province; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"GRASP; Scheduling (production processes); Figure of merit; Computer science; Greedy algorithm; Schedule; Greedy randomized adaptive search procedure; Job shop scheduling; Set (abstract data type); Constructive; Heuristic; Artificial intelligence; Mathematical optimization; Computer vision; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007068818,0.0008096864,0.0009362092,0.0006422835,0.0007070616,0.0007480076,0.0009867572,0.000841268,0.004171714],"category_scores_gemma":[0.001544962,0.0003855384,0.0006641687,0.0008039362,0.0005425049,0.0009584448,0.0007128172,0.0009035312,0.0005058362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075839,"about_ca_system_score_gemma":0.002079953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00613221,"about_ca_topic_score_gemma":0.005208595,"domain_scores_codex":[0.999662,0.00007907923,0.00001175734,0.00006686847,0.00009643616,0.00008381771],"domain_scores_gemma":[0.9995827,0.0001805996,0.00006169106,0.00004756285,0.00007495356,0.00005246989],"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":[0.00008174057,0.00005307053,0.0002972152,0.00008945345,0.0000251287,0.0000859939,0.00006894138,0.9155594,0.00350867,0.01369846,0.0035284,0.06300354],"study_design_scores_gemma":[0.00001790911,0.00007451172,0.0001069615,0.00001109894,0.000009904019,0.00004042067,0.00004002839,0.9889001,0.0007582112,0.007046788,0.002985663,0.000008491392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02458031,0.00045646,0.9683071,0.0002615596,0.00007507097,0.00008324558,0.00008039413,0.0006657529,0.005490202],"genre_scores_gemma":[0.3811547,0.0004262667,0.6135307,0.0001580836,0.00006080249,0.0001979568,0.0002481382,0.0002233303,0.004000037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00613221,"threshold_uncertainty_score":0.01395571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326210895807327,"score_gpt":0.2610357203408435,"score_spread":0.2284146307601108,"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."}}