{"id":"W4229053904","doi":"10.1111/2041-210x.13880","title":"Estimating animal size or distance in camera trap images: Photogrammetry using the pinhole camera model","year":2022,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Parks and Wilderness Society","funders":"Office of Polar Programs","keywords":"Camera trap; Pinhole camera model; Photogrammetry; Computer vision; Pinhole (optics); Artificial intelligence; Camera auto-calibration; Context (archaeology); Computer science; Field of view; Pixel; Camera resectioning; Wildlife; Geography; Ecology; Optics; Physics; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002205431,0.0001065011,0.0001667452,0.00005209483,0.0005335161,0.000009165082,0.0001488194,0.00008977044,0.0003356462],"category_scores_gemma":[0.0004429714,0.00009178981,0.00002440792,0.0004742596,0.0002995205,0.0001700308,0.0001932284,0.0004226044,0.000002121845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004955093,"about_ca_system_score_gemma":0.00004565188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104838,"about_ca_topic_score_gemma":0.002824527,"domain_scores_codex":[0.9980237,0.001021935,0.0002665728,0.0003047715,0.00008465763,0.0002983426],"domain_scores_gemma":[0.999083,0.0006466809,0.0001094996,0.0001310945,0.000004088635,0.00002560549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000142013,0.00008330221,0.8164037,0.000004772231,0.000003643876,0.000007767194,0.0006220351,0.1764155,0.00295609,0.00007772289,0.00008187503,0.003201554],"study_design_scores_gemma":[0.0002270557,0.00005543044,0.4901474,0.000002145612,0.000006493027,0.0000193561,0.0004552166,0.5064198,0.00002416107,0.002549321,0.00002653514,0.00006703147],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.830203,0.00005890161,0.168711,0.0004897082,0.0001660266,0.0002104599,0.000003876477,0.00001381931,0.0001431862],"genre_scores_gemma":[0.7857059,0.000004225622,0.2134709,0.0005990198,0.00001239146,0.0001076366,0.000001522902,0.000006204188,0.00009224954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3300044,"threshold_uncertainty_score":0.4103428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952556125431265,"score_gpt":0.3299585454773266,"score_spread":0.300432984223014,"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."}}