{"id":"W6964801996","doi":"10.26023/en7a-4099-y006","title":"WINTRE-MIX: CFI Climate Sentinels Arboretum MRR-2 Processed Data. Version 1.0","year":2022,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Sociology and Education Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Precipitation; Climate change; Radar; Doppler radar; Profiling (computer programming)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009364528,0.00252855,0.001105601,0.002489835,0.0008261852,0.001816992,0.00329195,0.002403923,0.02381422],"category_scores_gemma":[0.003202852,0.00062132,0.001165205,0.003998422,0.0004278227,0.001222187,0.001664845,0.001790429,0.04753108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637205,"about_ca_system_score_gemma":0.002411998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0798301,"about_ca_topic_score_gemma":0.1477871,"domain_scores_codex":[0.9990595,0.0001255764,0.00009236229,0.0002509351,0.0002807521,0.0001908098],"domain_scores_gemma":[0.9987192,0.0002369058,0.0001260151,0.0003081589,0.0004347341,0.0001751284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006861318,0.00003747292,0.001426159,0.0004130417,0.00003700958,0.00004244619,0.00003205577,0.0006541688,0.0003000383,0.0002954368,0.9943671,0.002326623],"study_design_scores_gemma":[0.0003302041,0.00004190347,0.01254601,0.0002957338,0.00004716427,0.000103162,0.0001694917,0.002548632,0.00103823,0.001142343,0.981656,0.00008114513],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000241638,0.00003946249,0.00006280521,0.00004506023,0.00002045158,0.00001171805,0.9987589,0.0004921809,0.0003277638],"genre_scores_gemma":[0.0003736943,0.0000192094,0.0002299901,0.00002259366,0.000004418283,0.00003832835,0.9990357,0.00005242972,0.0002236791],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0798301,"threshold_uncertainty_score":0.1587309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1423934939095641,"score_gpt":0.4515311498472275,"score_spread":0.3091376559376634,"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."}}