{"id":"W2700797120","doi":"10.1002/rse2.48","title":"A review of camera trapping for conservation behaviour research","year":2017,"lang":"en","type":"review","venue":"Remote Sensing in Ecology and Conservation","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":346,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Lottery Heritage Fund; Natural Environment Research Council; Sight Research UK","keywords":"Metadata; Camera trap; Relevance (law); Variety (cybernetics); Computer science; Data science; Ecology; Artificial intelligence; Habitat; Biology; World Wide Web; Political science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003780898,0.0002590926,0.001199563,0.0002216795,0.0004091281,0.0000212454,0.0002191711,0.0006397201,0.00004270714],"category_scores_gemma":[0.001978691,0.0002646166,0.0001546525,0.0003455105,0.0006109013,0.0001804968,0.0001276575,0.0005237203,0.00002859546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002431768,"about_ca_system_score_gemma":0.0002822617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274151,"about_ca_topic_score_gemma":0.006192543,"domain_scores_codex":[0.9971285,0.0008349539,0.0009606692,0.0005397935,0.0001633826,0.0003726932],"domain_scores_gemma":[0.9969764,0.001504094,0.0008879646,0.0004531673,0.0001282397,0.00005009456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001960641,0.00002914449,0.009550997,0.020629,0.00003518858,0.000005448963,0.00006114569,6.496482e-7,0.000002439564,0.00006348593,0.008265042,0.9613379],"study_design_scores_gemma":[0.0003303344,0.00008246105,0.02820414,0.04291758,0.0002890902,0.00007486913,0.00001358557,0.0009213901,6.972634e-7,0.001026317,0.9258505,0.0002889978],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00532134,0.9867532,0.0002873443,0.004302732,0.0002845925,0.002582529,0.00001483946,0.00002204069,0.0004314314],"genre_scores_gemma":[0.00009837392,0.994876,0.002740701,0.001658989,0.00004323325,0.00001570692,0.0001836162,0.0000260108,0.0003573663],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9610488,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1609735334528995,"score_gpt":0.403332001224093,"score_spread":0.2423584677711935,"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."}}