{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003815873,0.00009670179,0.0001607567,0.0001057624,0.000279323,0.00002103484,0.0003125889,0.00005811437,0.00001629526],"category_scores_gemma":[0.00002442733,0.0000348902,0.00001792623,0.001670775,0.000302479,0.0001540045,0.0004811718,0.0001355842,0.000008468732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001869381,"about_ca_system_score_gemma":0.000004436202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005467543,"about_ca_topic_score_gemma":0.004442502,"domain_scores_codex":[0.9989349,0.00003107793,0.000108133,0.0003483064,0.0001591297,0.0004184704],"domain_scores_gemma":[0.9997434,0.0001400743,0.00002527934,0.00002972365,0.000008450608,0.00005309002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000289717,0.0002165291,0.3159069,0.000008222724,0.000008415149,0.0006085447,0.002307294,0.0001646192,0.5779132,0.003228228,0.001805554,0.09754273],"study_design_scores_gemma":[0.0001413934,0.0001376179,0.9923808,0.00001378426,0.000001530524,0.00001157539,0.0003964044,0.004453279,0.0006375557,0.001579183,0.00009811988,0.0001487701],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982256,0.00005560385,9.607827e-8,0.0006930997,0.00005387044,0.00003746036,0.0002063075,0.00005401041,0.000674004],"genre_scores_gemma":[0.999385,0.0001915309,0.000005282509,0.0001769348,0.00002318228,0.000003845156,0.00009775179,1.852776e-7,0.0001162995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6764739,"threshold_uncertainty_score":0.247902,"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."}}