{"id":"W4402241614","doi":"10.48397/arriab.2021.21.xxi.073","title":"IMPROVEMENT OF TECHNOLOGY FOR ADAPTATION OF PLANT MICROCLONES TO EX VITRO CONDITIONS","year":2021,"lang":"ru","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptation (eye); Ex vivo; Computer science; In vitro; Biology; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008137453,0.001058238,0.0007468762,0.0007348403,0.0004239587,0.0009996122,0.0008116293,0.000657241,0.002511007],"category_scores_gemma":[0.001117004,0.0004394827,0.001100301,0.0006167493,0.000398386,0.0005699,0.0009052408,0.001091026,0.003130586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005350612,"about_ca_system_score_gemma":0.0005326647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009363711,"about_ca_topic_score_gemma":0.0009791981,"domain_scores_codex":[0.9993364,0.0001486137,0.00009450037,0.0001634166,0.0001721903,0.00008491324],"domain_scores_gemma":[0.9993736,0.0001848048,0.0001096346,0.0001597145,0.0001221675,0.00005017627],"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":[0.00002965827,0.00002295629,0.0002823247,0.0001309933,0.000008535849,0.0001107884,0.00005131249,0.0002630076,0.9937999,0.0001184975,0.00007019728,0.005111907],"study_design_scores_gemma":[0.000008283073,0.0002688408,0.004148619,0.0000402532,0.00006458291,0.0004982113,0.00009135904,0.001117927,0.9788021,0.0001270609,0.01481444,0.00001832657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5928119,0.008546836,0.37788,0.0007292159,0.0009200253,0.0008854195,0.002880759,0.002764006,0.01258188],"genre_scores_gemma":[0.6659259,0.01249487,0.287367,0.000245587,0.0001141723,0.001180562,0.007595236,0.0008871473,0.02418947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002511007,"threshold_uncertainty_score":0.008400142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817959825186585,"score_gpt":0.2323026620178158,"score_spread":0.2141230637659499,"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."}}