{"id":"W4393809031","doi":"10.5281/zenodo.8402822","title":"Global Phytoplankton Phenological Indices - 25km resolution","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Society of Intestinal Research","funders":"","keywords":"Phenology; Environmental science; Remote sensing; Resolution (logic); Phytoplankton; Climatology; Mathematics; Computer science; Geography; Geology; Ecology; Biology; Artificial intelligence","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":[0.0006736359,0.001928317,0.000993795,0.002446922,0.0004507824,0.0009651529,0.001786435,0.0008972398,0.02650593],"category_scores_gemma":[0.002020652,0.0006883276,0.0008570436,0.00673779,0.0002236451,0.0008314336,0.001031937,0.001152578,0.03298971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531944,"about_ca_system_score_gemma":0.001849443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07360747,"about_ca_topic_score_gemma":0.08043353,"domain_scores_codex":[0.9993376,0.00005515808,0.00009313871,0.0002131314,0.0001762643,0.0001245793],"domain_scores_gemma":[0.9987327,0.0001022302,0.0001970194,0.0002401041,0.0006154676,0.0001124163],"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":[0.00008494897,0.00003456507,0.004735192,0.0005671829,0.00006805614,0.0000325217,0.00003282808,0.0008149446,0.0004070977,0.0003797146,0.9892437,0.003599114],"study_design_scores_gemma":[0.0004369783,0.00002661648,0.08960733,0.000300595,0.00005982388,0.00009839315,0.000134212,0.001227053,0.00122419,0.0008275671,0.9060031,0.00005411724],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003553048,0.00001880051,0.00004409469,0.00001381723,0.000008087774,0.000007860342,0.9991181,0.00009377828,0.0003401867],"genre_scores_gemma":[0.0007369195,0.00001562998,0.0001943972,0.00001051522,0.000002408973,0.00004085246,0.998637,0.00002794683,0.0003342844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07360747,"threshold_uncertainty_score":0.1463581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424111969038168,"score_gpt":0.2321182359171976,"score_spread":0.1978771162268159,"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."}}