{"id":"W6967068991","doi":"10.5061/dryad.47d7wm3m0","title":"Density data for Lake Erie benthic invertebrate assemblages from 1930 to 2019","year":2024,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Benthic zone; Invertebrate; Trophic level; Taxon; Benthos; Abundance (ecology); Fauna","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004707075,0.0004378821,0.0004350523,0.003693165,0.0004000604,0.0006725611,0.0005684388,0.0002769349,0.005435003],"category_scores_gemma":[0.001611287,0.0003947711,0.0004231102,0.004870985,0.0001542935,0.0005477899,0.001202534,0.0004274203,0.002936803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777108,"about_ca_system_score_gemma":0.001203437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2721955,"about_ca_topic_score_gemma":0.4911801,"domain_scores_codex":[0.999498,0.00003195099,0.00008339576,0.0001095211,0.0001688185,0.0001082285],"domain_scores_gemma":[0.9980617,0.00009922807,0.0005108324,0.0001459301,0.001062183,0.0001200849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004733051,0.0001015376,0.744424,0.000923321,0.0005131898,0.0004043194,0.001280155,0.001788815,0.002881617,0.0007635946,0.2000295,0.04641657],"study_design_scores_gemma":[0.00002039571,0.00001700631,0.9280385,0.00009052427,0.00003549176,0.00008657605,0.0002859007,0.0002292424,0.0004754795,0.00002657389,0.07067431,0.00002006212],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2483024,0.0008633999,0.0008345715,0.0002076472,0.0000376716,0.0001007338,0.7337955,0.0002890527,0.01556897],"genre_scores_gemma":[0.2600349,0.001022832,0.002965966,0.000343188,0.00003267487,0.0006961262,0.7213542,0.00014276,0.01340739],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7278045,"threshold_uncertainty_score":0.5412222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1079890723936493,"score_gpt":0.3350012954978296,"score_spread":0.2270122231041803,"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."}}