{"id":"W6907332401","doi":"10.21966/7nwn-bj60","title":"Calliarthron 2023 Experiment - Environmental Data","year":2023,"lang":"en","type":"dataset","venue":"Hakai Institute","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coralline algae; Ocean acidification; Algae; Marine invertebrates; Invertebrate; Red algae; Biodiversity; Kelp; Crustose","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005364176,0.0009658655,0.000796875,0.0003650373,0.0003448415,0.000191552,0.00368747,0.0006173543,0.002073061],"category_scores_gemma":[0.0001120289,0.001020777,0.0001565534,0.00035946,0.0007028157,0.0008893874,0.004599071,0.0009627207,0.6038219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008329139,"about_ca_system_score_gemma":0.0003349748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005190363,"about_ca_topic_score_gemma":0.00465651,"domain_scores_codex":[0.9946918,0.0001205159,0.0007795539,0.002013341,0.00140734,0.0009874493],"domain_scores_gemma":[0.9923766,0.00004654366,0.0003919687,0.006793585,0.00001095384,0.0003803733],"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":[0.00003847019,0.0003197861,0.000002478271,0.00007731059,0.0002746745,0.001044584,0.00001427207,0.00004842444,0.0004476135,0.00001116723,0.9974833,0.0002378788],"study_design_scores_gemma":[0.000742964,0.00006313172,0.00005857972,0.0001899823,0.0002354159,0.00003823126,0.00004324003,0.00005943437,0.0001083718,0.000008417506,0.9974078,0.001044464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008810891,0.001036966,0.000007751548,0.00005777541,0.0053132,0.0008896987,0.9919379,0.0004012207,0.0002673285],"genre_scores_gemma":[0.00000976615,0.001746557,0.0001861422,0.0001621601,0.001403916,0.000215672,0.9931077,0.0002470246,0.002921092],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6017489,"threshold_uncertainty_score":0.9992242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09997886419743625,"score_gpt":0.335229451429936,"score_spread":0.2352505872324998,"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."}}