{"id":"W4392919103","doi":"10.3390/s24061917","title":"Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Plant and Biological Electrophysiology Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Software AG – Stiftung; Narodowe Centrum Nauki","keywords":"Computer science; Anger; Happiness; Artificial intelligence; Random forest; Set (abstract data type); Recall; Mel-frequency cepstrum; Enhanced Data Rates for GSM Evolution; Emotion classification; Parameterized complexity; Pattern recognition (psychology); Machine learning; Feature extraction; Psychology; Cognitive psychology","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":[0.0005680174,0.0007555319,0.0002924566,0.0004328951,0.0001528875,0.0005099523,0.0004220566,0.0004736703,0.001144476],"category_scores_gemma":[0.001559128,0.000224663,0.0004389376,0.0002332846,0.0003525107,0.0008193229,0.0006747281,0.0007139372,0.0005220402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003548213,"about_ca_system_score_gemma":0.0002904575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002603713,"about_ca_topic_score_gemma":0.003725298,"domain_scores_codex":[0.9997832,0.00004037147,0.000008325738,0.00007499163,0.00004755488,0.00004555201],"domain_scores_gemma":[0.9996597,0.0001667559,0.00003836531,0.00003757915,0.00007912875,0.00001854866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004750928,0.000255106,0.009215924,0.0002090513,0.0001564288,0.0002818149,0.0002910811,0.3185647,0.1363479,0.002554843,0.002924178,0.5287239],"study_design_scores_gemma":[0.000004708556,0.000109876,0.004466538,0.0000149212,0.00002781682,0.0000627625,0.0000342802,0.9802576,0.01227715,0.002149473,0.0005812615,0.00001359623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3546248,0.0008974383,0.6360321,0.0003853271,0.0001402227,0.0000724936,0.0002299524,0.002427066,0.005190545],"genre_scores_gemma":[0.9650604,0.0001730489,0.0327842,0.00008512793,0.00002430398,0.00002618096,0.0001962949,0.000035037,0.001615465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002603713,"threshold_uncertainty_score":0.005177081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263095331195455,"score_gpt":0.2261838134871135,"score_spread":0.199874280367568,"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."}}