{"id":"W1987899762","doi":"10.1016/j.earscirev.2013.02.001","title":"Concepts of hydrological connectivity: Research approaches, pathways and future agendas","year":2013,"lang":"en","type":"article","venue":"Earth-Science Reviews","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":701,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Manitoba","funders":"Durham University","keywords":"Surface runoff; Terrain; Hydrological modelling; Process (computing); Computer science; Conceptual framework; Variety (cybernetics); Environmental resource management; Environmental science; Hydrology (agriculture); Geography; Cartography; Ecology; Geology; Sociology","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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003942432,0.0001368667,0.0003271036,0.0000671045,0.000460833,0.00003523079,0.0003896467,0.00006674855,0.001449954],"category_scores_gemma":[0.0001895609,0.00009018101,0.00004883007,0.0006280422,0.003661489,0.0004345769,0.0007456968,0.0002367622,0.0009548572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002512109,"about_ca_system_score_gemma":0.00001017257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006545682,"about_ca_topic_score_gemma":0.00002001259,"domain_scores_codex":[0.9979016,0.0003609301,0.0002620773,0.0005394889,0.0004241597,0.00051176],"domain_scores_gemma":[0.9993257,0.00008935186,0.00009184946,0.0003451393,0.00001542644,0.0001325258],"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":[0.00002290457,0.0004268195,0.05072944,0.0002183283,0.00003145112,0.00002144411,0.007733778,0.00006956256,0.2214446,0.0172868,0.01414528,0.6878695],"study_design_scores_gemma":[0.0004027783,0.0005738267,0.1599328,0.00004091095,0.00002101108,0.00001869349,0.0007913318,0.0006887426,0.01180095,0.005782211,0.8195336,0.000413111],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9035333,0.005440222,0.0005051071,0.003615779,0.0001716,0.001787566,0.000002269612,0.00003864985,0.08490545],"genre_scores_gemma":[0.9909379,0.005162064,0.003034495,0.0002763595,0.00006239177,0.0001146586,8.500047e-7,0.000004857814,0.0004063989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8053884,"threshold_uncertainty_score":0.999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321090990351479,"score_gpt":0.319739034065733,"score_spread":0.1876299350305851,"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."}}