{"id":"W4412764082","doi":"10.1016/j.ecoinf.2025.103366","title":"Multilabel classification of peatland plant species from high-resolution drone images","year":2025,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke; Université de Montréal","funders":"Institut de Valorisation des Données; Québec Ministère du Développement Durable, de l’Environnement et de la Lutte Contre les Changements Climatiques; Société Française de lutte contre les Cancers et les leucémies de l'Enfant et de l'Adolescent","keywords":"Peat; Drone; High resolution; Remote sensing; Aerial imagery; Artificial intelligence; Computer science; Ecology; Geography; Biology; Botany","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002517557,0.000109165,0.0002099551,0.00003707558,0.00008155133,0.0000272934,0.0002151267,0.0001207696,0.000812455],"category_scores_gemma":[0.0001900912,0.00008202253,0.00003705465,0.000160772,0.0001612316,0.0002342936,0.0001641429,0.0001121815,0.0003860206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001794582,"about_ca_system_score_gemma":0.000007939073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002905689,"about_ca_topic_score_gemma":0.0001716563,"domain_scores_codex":[0.9989929,0.0000511213,0.0004840131,0.00009944,0.0001954149,0.0001771108],"domain_scores_gemma":[0.9992089,0.0003186529,0.0002103064,0.0002102194,0.00001100673,0.00004091631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00023781,0.001630239,0.7065715,0.0003850035,0.000170236,0.00001122156,0.002682117,0.006939837,0.1062345,0.007192577,0.1456443,0.02230068],"study_design_scores_gemma":[0.0003547008,0.00008151737,0.9055497,0.00002375012,0.00001424597,5.656043e-7,0.0001848637,0.08641345,0.003237242,0.0002854584,0.003764198,0.00009031012],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745793,0.00001387709,0.002862281,0.0001789725,0.0002326322,0.0003106041,0.0001031204,0.00005305645,0.02166612],"genre_scores_gemma":[0.9950238,0.00001907728,0.004110252,0.0001052566,0.00001771442,0.00002077076,0.0001115574,0.000002790147,0.000588781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1989783,"threshold_uncertainty_score":0.8895812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263879515459395,"score_gpt":0.2165834488584528,"score_spread":0.2039446537038588,"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."}}