{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001720206,0.0002062969,0.0001784088,0.00001219889,0.0005286725,0.00009733563,0.0001237813,0.00003003981,0.02133653],"category_scores_gemma":[0.00002746616,0.0001629796,0.00002556943,0.0002860283,0.0001366813,0.0002565624,0.0002131897,0.0001739467,0.0003030504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004270947,"about_ca_system_score_gemma":0.000009097587,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004927993,"about_ca_topic_score_gemma":0.02857802,"domain_scores_codex":[0.9986901,0.00009375317,0.0001599707,0.0004028412,0.0003650497,0.0002882295],"domain_scores_gemma":[0.999412,0.00007935965,0.00009776086,0.0002749149,0.00001305495,0.0001229199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003832574,0.003053842,0.8467403,0.00009102099,0.0002105738,0.01263729,0.02794355,0.007118067,0.001259057,0.0000141075,0.09547419,0.005074735],"study_design_scores_gemma":[0.006479742,0.002829017,0.6840354,0.00003023815,0.0001573124,0.004436002,0.2647995,0.02474691,0.0003686857,0.00001145747,0.0116725,0.0004333035],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917393,0.00003573767,0.0001074523,0.0001282997,0.0000401166,0.0006568315,0.00007439384,0.00007281298,0.007145036],"genre_scores_gemma":[0.9968125,0.000004852267,0.001059121,0.0001706085,0.000007566432,0.0001841097,0.00003678188,0.00002636109,0.001698122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2368559,"threshold_uncertainty_score":0.9891479,"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."}}