{"id":"W3200406703","doi":"10.3897/biss.5.74372","title":"Data Standards for the Phenology of Plant Specimens","year":2021,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Science Foundation","keywords":"Phenology; Herbarium; Digitization; Computer science; Database; Geography; Ecology; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001978676,0.00005244706,0.00007737831,0.00002848129,0.0005509864,0.00008913325,0.0004001322,0.00002629393,0.005168595],"category_scores_gemma":[0.0006503185,0.00003790444,0.00001686803,0.000346781,0.0007440879,0.001231332,0.0006261297,0.00003478034,0.00003593498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004686661,"about_ca_system_score_gemma":0.000308273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000341946,"about_ca_topic_score_gemma":0.0001181244,"domain_scores_codex":[0.9983648,0.000008597824,0.0001329137,0.0001251091,0.001211691,0.0001568657],"domain_scores_gemma":[0.9990327,0.00004489293,0.00008082965,0.0002908226,0.0004970679,0.00005373199],"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":[0.0001541136,0.00003942847,0.007019108,0.00003566749,0.00002044234,0.000001566814,0.00310712,0.00002053073,0.001483147,0.006933199,0.9685313,0.01265438],"study_design_scores_gemma":[0.00035486,0.00003546607,0.01392052,0.000001902684,0.00001235634,0.00000587162,0.009820846,0.0003018223,0.001494958,0.00001606964,0.9739765,0.00005882738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4315269,0.0002359947,0.009104697,0.009464707,0.0008787879,0.000744249,0.4792969,0.00005855675,0.06868915],"genre_scores_gemma":[0.9939147,0.001374046,0.0005099303,0.002588216,0.00002575995,0.000007261689,0.001524781,0.000001946844,0.00005334769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5623878,"threshold_uncertainty_score":0.9957408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05637819301971513,"score_gpt":0.2837233119668771,"score_spread":0.227345118947162,"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."}}