{"id":"W6902242755","doi":"10.6084/m9.figshare.21770123.v1","title":"Data-extraction sheet for \"How do natural changes in flow magnitude affect fish abundance and biomass in temperate regions? A systematic review\"","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais; Carleton University","funders":"","keywords":"Extraction (chemistry); Biomass (ecology); Temperate climate; Table (database); Abundance (ecology); Process (computing); Fish <Actinopterygii>; Flow (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01229277,0.00265878,0.005276794,0.0188994,0.001268133,0.00420689,0.003022511,0.002288877,0.4276356],"category_scores_gemma":[0.07904959,0.002390019,0.006082014,0.02188296,0.0007689163,0.003441319,0.003774268,0.002008175,0.05773138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003669687,"about_ca_system_score_gemma":0.01229474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009726652,"about_ca_topic_score_gemma":0.01503724,"domain_scores_codex":[0.99094,0.001775825,0.004638895,0.001031811,0.001205223,0.0004081416],"domain_scores_gemma":[0.9459419,0.03517286,0.00604475,0.003991602,0.008139929,0.0007089078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0004583295,0.00003673813,0.0006453706,0.3400521,0.0008362053,0.0001415646,0.0003248608,0.0004178862,0.0006037713,0.003149049,0.6390713,0.01426282],"study_design_scores_gemma":[0.005068103,0.0001399758,0.007348252,0.09242543,0.002350317,0.0001554863,0.0004953513,0.0004033189,0.0008599025,0.0061323,0.8843939,0.0002277772],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001408271,0.0003988661,0.0006305826,0.0002872252,0.00009943007,0.004504177,0.9923826,0.0002899214,0.001266413],"genre_scores_gemma":[0.003586546,0.003026051,0.02211474,0.001651383,0.0001866014,0.2591622,0.69799,0.001041378,0.01124103],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4276356,"threshold_uncertainty_score":0.8164083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09220765994903908,"score_gpt":0.3502048874726187,"score_spread":0.2579972275235797,"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."}}