{"id":"W7002277144","doi":"","title":"Momentum Shift: Canada’s M&A Landscape in 2025","year":2025,"lang":"en","type":"other","venue":"","topic":"Nuclear Structure and Function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Momentum (technical analysis); Population; Stability (learning theory); Perspective (graphical)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002979944,0.0007701511,0.0004100423,0.001280979,0.004309238,0.01049707,0.001790456,0.006435193,0.1251037],"category_scores_gemma":[0.00688954,0.0002857546,0.0004269903,0.001898888,0.005605397,0.004270882,0.004154776,0.004859915,0.02123476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02810569,"about_ca_system_score_gemma":0.05334799,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5151315,"about_ca_topic_score_gemma":0.6119146,"domain_scores_codex":[0.9978656,0.0001803829,0.00003626555,0.000187359,0.001113843,0.0006165082],"domain_scores_gemma":[0.9959456,0.0003311341,0.0000821657,0.0001499486,0.00133173,0.002159349],"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":[0.00002850479,0.00001366405,0.0001688026,0.00003894791,0.000002646402,0.00005833986,0.00007759321,0.0001262698,0.0001515245,0.114894,0.8379353,0.0465044],"study_design_scores_gemma":[0.000003670029,0.000003154321,0.0001928143,0.00004227848,8.966201e-7,0.00001820747,0.00005873459,0.00005298915,0.00007728022,0.008676718,0.9908671,0.00000615274],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002031986,0.0143695,0.001871961,0.3228384,0.01291122,0.00003269119,0.001816038,0.0005337698,0.6435944],"genre_scores_gemma":[0.04337028,0.01331546,0.003234017,0.1130511,0.005077043,0.00004897509,0.001526043,0.0007264456,0.8196508],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4848685,"threshold_uncertainty_score":0.9754479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005891064952880433,"score_gpt":0.2226311619081397,"score_spread":0.2167400969552593,"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."}}