{"id":"W4411010200","doi":"10.1016/j.biocon.2025.111207","title":"Estimating the landscape-scale abundance of an arboreal folivore using thermal imaging drones and binomial N-mixture modelling","year":2025,"lang":"en","type":"article","venue":"Biological Conservation","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of Environment and Conservation","funders":"National Parks and Wildlife Service; Royal Zoological Society of New South Wales; Taronga Conservation Society Australia; Ecological Society of Australia; Paddy Pallin Foundation; Commonwealth Scientific and Industrial Research Organisation; Equity Trustees; Australian Academy of Science; Holsworth Wildlife Research Endowment","keywords":"Arboreal locomotion; Abundance (ecology); Scale (ratio); Ecology; Geography; Environmental science; Biology; Habitat; Cartography","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":[0.00647164,0.000877935,0.0009614643,0.001701894,0.0004999755,0.001283423,0.001765502,0.000893152,0.0008861173],"category_scores_gemma":[0.01746781,0.001007581,0.002032588,0.0008489977,0.0009797356,0.001418971,0.001545312,0.0009929002,0.0002555127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008725047,"about_ca_system_score_gemma":0.0005928549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02565461,"about_ca_topic_score_gemma":0.03234654,"domain_scores_codex":[0.9978074,0.001231793,0.0001354635,0.0004978447,0.0002085543,0.0001189823],"domain_scores_gemma":[0.9914162,0.006137052,0.001277868,0.000630137,0.000389434,0.0001493946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003619436,0.0001495627,0.1733988,0.0001261545,0.0006660839,0.000137797,0.0005853578,0.758621,0.005373295,0.005188098,0.0003377272,0.0550542],"study_design_scores_gemma":[0.000007841309,0.0000324994,0.01346593,0.00001011609,0.0000284986,0.00004464029,0.00003505735,0.9842324,0.0003561169,0.001618387,0.0001433156,0.00002522665],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4554361,0.0002305948,0.5427654,0.0001331881,0.00001900388,0.0001139038,0.0002868097,0.0003517994,0.0006632919],"genre_scores_gemma":[0.8839415,0.0001227066,0.1142837,0.00004527079,0.00002306647,0.0001413734,0.0005917302,0.00004061547,0.0008099651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02565461,"threshold_uncertainty_score":0.05101055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193879549293963,"score_gpt":0.2416756260330433,"score_spread":0.222287671103647,"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."}}