{"id":"W4385483792","doi":"10.1016/j.tplants.2023.06.016","title":"Plant blindness and diversity in AI language models","year":2023,"lang":"en","type":"article","venue":"Trends in Plant Science","topic":"Plant and Biological Electrophysiology Studies","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Generative grammar; Biology; Blindness; Plant biology; Plant science; Generative model; Diversity (politics); Plant diversity; Cognitive science; Artificial intelligence; Computer science; Ecology; Biodiversity; Botany; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002557771,0.0004695793,0.001235011,0.001320063,0.001018259,0.00224716,0.001216852,0.001783553,0.004552261],"category_scores_gemma":[0.02111926,0.000522413,0.0008116281,0.0004982884,0.002936609,0.004472367,0.002814443,0.002577732,0.0003468728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020147,"about_ca_system_score_gemma":0.0006999166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860388,"about_ca_topic_score_gemma":0.002041505,"domain_scores_codex":[0.9986615,0.0006838733,0.00005019803,0.0002009195,0.0001860706,0.000217429],"domain_scores_gemma":[0.9835954,0.01317042,0.0008052549,0.001084928,0.0005492612,0.0007947425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002767844,0.00007363301,0.003981615,0.00009797237,0.00009298007,0.0003713695,0.001503621,0.1697181,0.003206632,0.7995077,0.001588281,0.01958131],"study_design_scores_gemma":[0.00002134865,0.00003624498,0.0006238789,0.00001113834,0.00001542231,0.0001643218,0.0001258866,0.3788777,0.0003155568,0.6193874,0.0003999151,0.00002120342],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5957786,0.0004568199,0.3675423,0.004334379,0.00006616336,0.00004282405,0.0003319065,0.0006518224,0.03079516],"genre_scores_gemma":[0.9914519,0.00008575946,0.006233657,0.0001612659,0.00003305988,0.00003213239,0.00007819577,0.0000637416,0.001860361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004552261,"threshold_uncertainty_score":0.01522887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06416670510396269,"score_gpt":0.2631745151566421,"score_spread":0.1990078100526795,"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."}}