{"id":"W2159201835","doi":"10.1098/rstb.2010.0079","title":"Correlation and studies of habitat selection: problem, red herring or opportunity?","year":2010,"lang":"en","type":"review","venue":"Philosophical Transactions of the Royal Society B Biological Sciences","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":305,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Environment Research Council; Fondazione Edmund Mach","keywords":"Computer science; Context (archaeology); Selection (genetic algorithm); Variance (accounting); Model selection; Autocorrelation; Range (aeronautics); Scale (ratio); Inference; Resource (disambiguation); Sampling (signal processing); Econometrics; Data mining; Ecology; Data science; Machine learning; Statistics; Artificial intelligence; Mathematics; Geography; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004937601,0.001076809,0.003212261,0.00393374,0.0004125265,0.001681534,0.002097321,0.002217416,0.002096319],"category_scores_gemma":[0.01145244,0.0004897644,0.0007276086,0.008508048,0.003720042,0.004224753,0.001098171,0.002092267,0.001071487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456509,"about_ca_system_score_gemma":0.002517004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004972858,"about_ca_topic_score_gemma":0.006013372,"domain_scores_codex":[0.9983799,0.0007210731,0.0001624696,0.0003359063,0.0003526314,0.00004793354],"domain_scores_gemma":[0.9873036,0.01042318,0.0006501228,0.0003977878,0.0009977289,0.0002275978],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003746637,0.00003964052,0.001029498,0.01528818,0.0002363922,0.0001485044,0.0003539839,0.0007006871,0.0002271542,0.0321057,0.02194356,0.9278893],"study_design_scores_gemma":[0.00002800581,0.0001242608,0.007646992,0.01711773,0.0002788779,0.001456902,0.0008040197,0.0004195293,0.0002991603,0.1000695,0.871654,0.0001010589],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008981022,0.9976665,0.0005995728,0.0009064444,0.0002017911,0.000003261708,0.000011167,0.00000381341,0.0005177091],"genre_scores_gemma":[0.001389259,0.9965545,0.0007972066,0.0004972796,0.0004077698,0.00001360079,0.00001740232,0.000003450573,0.0003196348],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9950624,"threshold_uncertainty_score":0.02611285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1497651337775354,"score_gpt":0.332726503112265,"score_spread":0.1829613693347296,"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."}}