{"id":"W7115926036","doi":"10.71892/11143/1250","title":"Détection du grand héron par imagerie aérienne et apprentissage profond : apports de l’imagerie visible à très haute résolution et de la fusion visible–thermique","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fluorescent labelling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006861303,0.0008497277,0.000488504,0.001020141,0.0003712709,0.0008750975,0.0004731125,0.001020445,0.002926743],"category_scores_gemma":[0.00104755,0.0004660297,0.0006320082,0.0004843987,0.0004036656,0.0007879846,0.0005928314,0.0007246741,0.0010787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004303618,"about_ca_system_score_gemma":0.0004102048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008672443,"about_ca_topic_score_gemma":0.0158914,"domain_scores_codex":[0.9997023,0.000026002,0.000007612222,0.00009110615,0.0001168602,0.00005602454],"domain_scores_gemma":[0.9995555,0.0001479373,0.00003889573,0.00002689269,0.000181132,0.00004961407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006877009,0.00006614927,0.009396612,0.0004314515,0.0001062746,0.0007429341,0.0007267065,0.003440604,0.9147347,0.0002715868,0.000970423,0.06842476],"study_design_scores_gemma":[0.0000669095,0.001398531,0.1581994,0.0001783893,0.000407195,0.003967797,0.002293249,0.1153742,0.701189,0.0009013233,0.01573673,0.0002873802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9069268,0.002697178,0.07973473,0.0003381404,0.0001296748,0.0001373884,0.0006374773,0.001748701,0.00764998],"genre_scores_gemma":[0.8543401,0.001808121,0.1307658,0.0002398179,0.00004938122,0.00008669005,0.0007415032,0.0003924387,0.01157605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008672443,"threshold_uncertainty_score":0.01724392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241624881724859,"score_gpt":0.3148869571542702,"score_spread":0.3024707083370217,"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."}}