{"id":"W4253648685","doi":"10.3410/f.5840958.5828055","title":"Faculty Opinions recommendation of Challenges of modeling depth-integrated marine primary productivity over multiple decades: A case study at BATS and HOT.","year":2010,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Productivity; Primary productivity; Oceanography; Data science; Computer science; Environmental science; Geography; Operations research; Engineering; Geology; Ecology; Biology; Economics; Ecosystem; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003510755,0.001441401,0.001044432,0.00224629,0.000691556,0.002426242,0.003217,0.002410337,0.02006087],"category_scores_gemma":[0.01816546,0.0006159475,0.001320146,0.003876406,0.0003211012,0.001691004,0.001805978,0.001910789,0.02780253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002220825,"about_ca_system_score_gemma":0.002799938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08332093,"about_ca_topic_score_gemma":0.2023506,"domain_scores_codex":[0.9988066,0.0002597336,0.0001169187,0.0003414568,0.0003608742,0.0001144115],"domain_scores_gemma":[0.9933586,0.00226745,0.0004937772,0.001615131,0.001673722,0.0005913508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005153425,0.00002917484,0.003897204,0.0001944857,0.00004506461,0.00001658661,0.00001843526,0.001041645,0.00007524867,0.000261096,0.9896924,0.004677018],"study_design_scores_gemma":[0.0003171058,0.00003196296,0.0259886,0.0003525567,0.00008886471,0.00009105218,0.0001849808,0.009028944,0.0006526289,0.002610838,0.9605837,0.00006868674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0011999,0.0001426025,0.0004726457,0.001054747,0.0001584789,0.0000253202,0.993828,0.0007954559,0.002322996],"genre_scores_gemma":[0.002992241,0.0001119749,0.001503216,0.0002386401,0.00003794439,0.00006777485,0.9927472,0.000122749,0.002178161],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08332093,"threshold_uncertainty_score":0.1656719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03488030959471329,"score_gpt":0.3199712645958866,"score_spread":0.2850909550011733,"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."}}