{"id":"W4233562686","doi":"10.3410/f.13399956.14770054","title":"Faculty Opinions recommendation of An algorithm for oceanic front detection in chlorophyll and SST satellite imagery.","year":2011,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Zoo","funders":"","keywords":"Satellite; Front (military); Satellite imagery; Computer science; Chlorophyll a; Remote sensing; Algorithm; Environmental science; Oceanography; Meteorology; Geology; Geography; Physics; Astronomy","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001447967,0.002928065,0.001286073,0.003724843,0.0006806091,0.00192249,0.002996139,0.002332547,0.0420901],"category_scores_gemma":[0.00617158,0.0008855589,0.002071802,0.003919469,0.0003177511,0.001354191,0.001398685,0.002116027,0.06888598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329698,"about_ca_system_score_gemma":0.002438113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02395038,"about_ca_topic_score_gemma":0.07819173,"domain_scores_codex":[0.9990677,0.00009808067,0.0001001125,0.000323402,0.0002763665,0.0001343413],"domain_scores_gemma":[0.9979456,0.0003564431,0.0001225992,0.0005792437,0.0007350872,0.0002609808],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006770119,0.00005207535,0.0007557041,0.000237713,0.00003963644,0.00001330736,0.000006205998,0.0005537022,0.0002178222,0.0001266621,0.9903979,0.007531499],"study_design_scores_gemma":[0.0006648594,0.00007396829,0.01024137,0.0002619054,0.00009867363,0.000129117,0.0000719294,0.01505424,0.003832522,0.00195523,0.9675332,0.00008288133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007813866,0.00008236265,0.001185626,0.0002176375,0.0001757427,0.0001026375,0.9911213,0.004421593,0.001911636],"genre_scores_gemma":[0.0008143348,0.00004352106,0.003167295,0.00005488162,0.00001615971,0.00009314148,0.9942548,0.0001743545,0.001381585],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.998552,"threshold_uncertainty_score":0.1408054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08104551932668208,"score_gpt":0.3968203157238728,"score_spread":0.3157747963971907,"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."}}