{"id":"W6889770929","doi":"10.26071/ogsl-0925e105-860e","title":"The Hickey Marsh - Characterization of Important Coastal Habitats on the North Shore of the St. Lawrence Maritime Estuary","year":2022,"lang":"en","type":"dataset","venue":"OGSL repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hickey; Estuary; Marsh; Shore; Habitat; Baseline (sea); Salt marsh","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":[0.000553593,0.0004645984,0.0003407199,0.003186282,0.0008182182,0.001725057,0.0008928912,0.0003635417,0.007314762],"category_scores_gemma":[0.001547402,0.0002953142,0.0005097871,0.005336552,0.0003371262,0.001039071,0.002165109,0.0005654222,0.006349741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001812257,"about_ca_system_score_gemma":0.003284189,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4123307,"about_ca_topic_score_gemma":0.7180454,"domain_scores_codex":[0.9994627,0.00005028048,0.00005181821,0.0001457711,0.0001682767,0.0001211779],"domain_scores_gemma":[0.99843,0.00008203371,0.0002062205,0.0002381799,0.0007962639,0.0002472816],"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":[0.0002710209,0.000135843,0.2800116,0.0007588923,0.0001376212,0.0003308793,0.001806364,0.001637226,0.001698821,0.003188068,0.6311093,0.07891438],"study_design_scores_gemma":[0.00006233715,0.00003874383,0.4201315,0.0002803845,0.00004604561,0.0001316068,0.002625948,0.00251278,0.001041039,0.0006527979,0.5724287,0.00004820939],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1128169,0.0005110137,0.002170524,0.0004742875,0.00009254948,0.0002056641,0.8610148,0.001882246,0.02083202],"genre_scores_gemma":[0.07708453,0.0003219917,0.006840252,0.00009073492,0.00001664737,0.0002305736,0.9034389,0.0003330347,0.0116434],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5876693,"threshold_uncertainty_score":0.8198613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009799602456196915,"score_gpt":0.2164026281132845,"score_spread":0.2066030256570876,"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."}}