{"id":"W4290739521","doi":"10.1002/ecs2.4174","title":"Multi‐image flock size estimation with <scp>CountEm</scp>: A case study with half a million Common Eiders and Greater Snow Geese","year":2022,"lang":"en","type":"article","venue":"Ecosphere","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Universidad de Cantabria","keywords":"Flock; Snow; Range (aeronautics); Abundance (ecology); Sample (material); Population; Statistics; Ecology; Estimation; Eider; Physical geography; Geography; Environmental science; Biology; Mathematics; Meteorology; Engineering; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001283331,0.0003379833,0.0003360358,0.001045805,0.0003543499,0.0003544901,0.0007718517,0.0003778819,0.001378205],"category_scores_gemma":[0.002704342,0.0002151003,0.0002315433,0.0007417688,0.0002019797,0.0004674011,0.0004321375,0.0002109333,0.0001926095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003614562,"about_ca_system_score_gemma":0.0002079267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02191931,"about_ca_topic_score_gemma":0.04437664,"domain_scores_codex":[0.9996588,0.0001179964,0.00002296499,0.00008037724,0.0000913403,0.00002851321],"domain_scores_gemma":[0.9976017,0.001260147,0.0002077731,0.0002784767,0.0005291331,0.0001228265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006367359,0.00103265,0.5610573,0.0003205415,0.0002887126,0.006565874,0.002442043,0.05755854,0.04204688,0.0007301178,0.005944638,0.321376],"study_design_scores_gemma":[0.00008029855,0.0004718013,0.5122486,0.000034996,0.00008831577,0.00346065,0.001425913,0.4568704,0.01966102,0.0006885013,0.0049032,0.0000663184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798521,0.00006266915,0.01857671,0.00009748578,0.000006692695,0.00009957295,0.0003486988,0.0002917388,0.0006643808],"genre_scores_gemma":[0.9400733,0.00003270847,0.0588348,0.00003231033,0.000007328118,0.00004590175,0.0003359021,0.00004658371,0.0005912891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02191931,"threshold_uncertainty_score":0.04358345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444815364399792,"score_gpt":0.2320242852125783,"score_spread":0.2175761315685804,"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."}}