{"id":"W759899959","doi":"","title":"Fishing for facts in Atlantic Canada: information sources for social scientists.","year":2006,"lang":"en","type":"article","venue":"Open Access Server of the Woods Hole Scientific Community (Woods Hole Scientific Community)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Fishing; Geography; Political science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003199945,0.0003951359,0.0004285043,0.01585171,0.00461342,0.004010562,0.001322022,0.0007934096,0.03076508],"category_scores_gemma":[0.01546693,0.0003728578,0.0002468134,0.04114232,0.0007281725,0.002091333,0.002250531,0.0007561734,0.004547387],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01965811,"about_ca_system_score_gemma":0.1067049,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9845598,"about_ca_topic_score_gemma":0.992396,"domain_scores_codex":[0.9980342,0.0001606908,0.0002402592,0.0001437949,0.001096663,0.0003243674],"domain_scores_gemma":[0.9793012,0.003979826,0.001249169,0.001086922,0.01218948,0.002193417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002144316,0.00005943725,0.07764123,0.001198741,0.00008717809,0.0003908247,0.01327828,0.0004919884,0.0007128884,0.006564815,0.7119628,0.1873973],"study_design_scores_gemma":[0.00002709849,0.000009049917,0.1363112,0.0009539089,0.00008274081,0.00006694098,0.01380629,0.0007905698,0.0007197437,0.00158898,0.8455729,0.00007061091],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.04046426,0.003811693,0.002723732,0.005806675,0.0002063913,0.000479564,0.8162792,0.001405012,0.1288233],"genre_scores_gemma":[0.24853,0.01345004,0.02528247,0.001339138,0.0001871882,0.001163093,0.5559831,0.0009171625,0.1531478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9959894,"threshold_uncertainty_score":0.1426302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128375446300418,"score_gpt":0.3246187908243287,"score_spread":0.2117812461942869,"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."}}