{"id":"W4387438388","doi":"10.2139/ssrn.4427295","title":"Designing Airdrops","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Political science; Computer science; Business","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":[0.0007429906,0.000870389,0.0005598902,0.0008704645,0.001376559,0.002054833,0.001268643,0.001144717,0.01518777],"category_scores_gemma":[0.002542939,0.0005044295,0.0004662601,0.0004533898,0.001025257,0.00282188,0.003771565,0.0008908101,0.004682349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004064105,"about_ca_system_score_gemma":0.0006010666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007369256,"about_ca_topic_score_gemma":0.001036315,"domain_scores_codex":[0.9992537,0.0001415413,0.00003164633,0.0001426855,0.0002598541,0.000170574],"domain_scores_gemma":[0.9991918,0.0002581809,0.00006062219,0.0001726371,0.0002178069,0.00009883563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005462219,0.0002862157,0.003385481,0.0009865243,0.00008475058,0.0009848108,0.001776321,0.09115172,0.1555029,0.3003974,0.02286087,0.4220367],"study_design_scores_gemma":[0.0001484997,0.001321155,0.001243869,0.0004355825,0.000111041,0.0008500683,0.002257465,0.3970019,0.1594755,0.1176433,0.3194046,0.0001069665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0456044,0.0009385105,0.8812369,0.0003506429,0.0004824547,0.0003653078,0.0001790772,0.00293853,0.0679041],"genre_scores_gemma":[0.6038429,0.001152874,0.3477022,0.0004242914,0.0001427242,0.0004122212,0.0004936216,0.0009964509,0.0448327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01518777,"threshold_uncertainty_score":0.05080813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005567354704116062,"score_gpt":0.1848463391191845,"score_spread":0.1792789844150684,"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."}}