{"id":"W4210973924","doi":"10.1139/er-2021-0121","title":"Estimating the numbers of aquatic birds affected by oil spills: pre-planning, response, and post-incident considerations","year":2022,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; York University","funders":"York University","keywords":"Shore; Sampling (signal processing); Transect; Environmental science; Oil spill; Population; Environmental resource management; Submarine pipeline; Fishery; Ecology; Geography; Computer science; Biology; Environmental protection; Environmental health; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.0009990886,0.0001546535,0.0002285531,0.00001799129,0.0005786808,0.00001233303,0.0001654181,0.00003464227,0.004997863],"category_scores_gemma":[0.0001971708,0.000118048,0.00006488464,0.00008527418,0.0004311292,0.000108242,0.0003306125,0.0002211331,0.0001718322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000195323,"about_ca_system_score_gemma":0.000008428509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005306342,"about_ca_topic_score_gemma":0.00002235092,"domain_scores_codex":[0.9981474,0.0007331222,0.0003847759,0.0002790305,0.0002592562,0.0001964682],"domain_scores_gemma":[0.9991372,0.0002431816,0.000272931,0.0002796838,2.413236e-7,0.00006681625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008669696,0.0005208737,0.7446091,0.0000183237,0.00002292093,0.00002680755,0.003913248,0.0005453421,0.2038264,0.000002167455,0.01617513,0.03025297],"study_design_scores_gemma":[0.0003329992,0.0003446013,0.9735175,0.00002444011,0.00009171418,0.0001055177,0.0004424852,0.0003777092,0.0008647392,0.0000402875,0.02364189,0.0002161089],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964418,0.002381182,0.0000397211,0.0003834342,0.00009060592,0.0004028234,0.00003173039,0.00001404134,0.0002145981],"genre_scores_gemma":[0.9959263,0.0001364116,0.001329977,0.0006895683,0.000007930335,0.0002291187,0.00002594159,0.00001544024,0.00163927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2289084,"threshold_uncertainty_score":0.9959117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125826812978364,"score_gpt":0.2648190076775379,"score_spread":0.2522363263797015,"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."}}