{"id":"W4396529586","doi":"10.1371/journal.pone.0302424","title":"Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec); Ministère des Ressources naturelles et des Forêts; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Université de Montréal; Ministère des Ressources Naturelles et de la Faune; Mitacs","keywords":"Pollen; Convolutional neural network; Computer science; Artificial intelligence; Identification (biology); Artificial neural network; Machine learning; Pattern recognition (psychology); Paleontology; Biology; Ecology","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":[0.0005139352,0.0009169001,0.0003388152,0.0008253574,0.0003718263,0.0004656865,0.0007023166,0.0007266094,0.00138813],"category_scores_gemma":[0.001331039,0.0002783232,0.0005341928,0.0007470602,0.0002695732,0.0006647976,0.0005290824,0.0007566499,0.0008456028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008529177,"about_ca_system_score_gemma":0.0005930575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02521663,"about_ca_topic_score_gemma":0.03739413,"domain_scores_codex":[0.9998335,0.00001602764,0.000009832453,0.000056743,0.00004627776,0.00003771268],"domain_scores_gemma":[0.9996474,0.000126321,0.00002344858,0.00005600031,0.000121946,0.00002492858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006891589,0.0003215816,0.02594153,0.0002746544,0.0002571526,0.001413651,0.0002840641,0.2849932,0.07327422,0.001673601,0.01061703,0.6002603],"study_design_scores_gemma":[0.00001296385,0.00007114341,0.006629045,0.00002057587,0.00002872744,0.0001273536,0.00004740719,0.9734509,0.01657472,0.0008596282,0.002161249,0.00001635802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.868224,0.002189869,0.111731,0.0005329204,0.0002452973,0.0001380902,0.002572094,0.007263725,0.007103003],"genre_scores_gemma":[0.8708905,0.0006830526,0.1178036,0.000130775,0.00004381417,0.00005261518,0.004284369,0.0001936227,0.005917653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02521663,"threshold_uncertainty_score":0.05013973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0731029700690163,"score_gpt":0.2597479564293927,"score_spread":0.1866449863603765,"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."}}