{"id":"W4283011987","doi":"10.14236/ewic/eva2022.19","title":"Hy-breed: Growing a responsive organo-mechanical agent","year":2022,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Breed; Computer science; Animal science; Biology","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":[0.0002771848,0.0003929521,0.0002463742,0.0001903908,0.0003623116,0.0007230462,0.0008508753,0.0007340211,0.006612307],"category_scores_gemma":[0.0003801378,0.0002417299,0.0003735933,0.00008422254,0.0003216018,0.001057824,0.001349021,0.0009542097,0.002619312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766924,"about_ca_system_score_gemma":0.000217352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002528466,"about_ca_topic_score_gemma":0.0005017106,"domain_scores_codex":[0.9998436,0.00001900541,0.000004702375,0.00003240566,0.00007743333,0.00002285211],"domain_scores_gemma":[0.9998648,0.00002867432,0.00001017721,0.0000281864,0.00002868862,0.00003944994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003189844,0.0004299407,0.0008252182,0.0003553992,0.00005434409,0.0004694496,0.0004197582,0.01047708,0.8296986,0.02404251,0.02184887,0.1110599],"study_design_scores_gemma":[0.00008487216,0.0005278328,0.0007007886,0.0000415879,0.00006854011,0.0006034712,0.0002528637,0.1136882,0.5776916,0.008021734,0.2982551,0.00006341851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2523173,0.001096105,0.6226418,0.001369389,0.001191167,0.0004645369,0.0009000218,0.02699322,0.09302647],"genre_scores_gemma":[0.5349789,0.0007625407,0.3297103,0.0006900732,0.0001127844,0.0003476699,0.001474772,0.002607736,0.1293152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006612307,"threshold_uncertainty_score":0.02212036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006640981313356544,"score_gpt":0.2174726131090597,"score_spread":0.2108316317957031,"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."}}