{"id":"W2885123433","doi":"10.1080/14634988.2018.1507408","title":"Integrated restoration prioritization–A multi-discipline approach in the Greater Toronto Area","year":2018,"lang":"en","type":"article","venue":"Aquatic Ecosystem Health & Management","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto and Region Conservation Authority","funders":"Environment and Climate Change Canada","keywords":"Watershed; Environmental resource management; Restoration ecology; Wildlife; Ecosystem services; Prioritization; Upstream (networking); Environmental planning; Habitat; Watershed management; Natural resource; Ecosystem; Environmental science; Business; Ecology; Computer science; Process management","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001658277,0.0001899654,0.0002385787,0.00004545613,0.0003031933,0.00009293163,0.0004165479,0.00005216551,0.0003830905],"category_scores_gemma":[0.000005753675,0.0001188031,0.00003423362,0.000365106,0.00001392259,0.0003455363,0.0001327311,0.00006742632,0.0006692465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007669993,"about_ca_system_score_gemma":0.00001503101,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005939571,"about_ca_topic_score_gemma":0.1750936,"domain_scores_codex":[0.9977995,0.0003449019,0.0006284911,0.0004244341,0.0003975155,0.0004051509],"domain_scores_gemma":[0.9991283,0.00002718865,0.0002138781,0.0005333858,0.00001146762,0.00008571283],"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":[0.0003678668,0.004045031,0.708868,0.008287755,0.0004154071,0.0001054802,0.1209696,0.009024117,0.00005558238,0.004758423,0.06562849,0.07747426],"study_design_scores_gemma":[0.001982897,0.00038188,0.2402305,0.0005410215,0.00004971671,0.00001542963,0.01365823,0.7179164,0.000009293214,0.0002392619,0.02445345,0.0005218998],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9512455,0.0002204891,0.01731562,0.002151096,0.0005035927,0.004044349,0.00001883911,0.0001295155,0.02437101],"genre_scores_gemma":[0.9958492,0.00004539223,0.002647658,0.0008257954,0.00008827935,0.0002558243,0.00008979824,0.00001612982,0.000181905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7088923,"threshold_uncertainty_score":0.8978895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111362786778221,"score_gpt":0.2720817633200738,"score_spread":0.2409681354522916,"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."}}