{"id":"W7074640020","doi":"","title":"Using community photography to investigate phenology: A case study of coat molt in the mountain goat (Oreamnos americanus) with missing data","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Water Quality and Resources Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Citizen science; Coat; Phenology; Photography; Offspring; Missing data; Period (music)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002628588,0.0003039675,0.0002504149,0.001787827,0.003732686,0.0008135244,0.001087648,0.001088099,0.001034487],"category_scores_gemma":[0.005540194,0.0003866829,0.0002999539,0.001536355,0.001663003,0.0008402278,0.001613038,0.0007860609,0.0001325504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002266225,"about_ca_system_score_gemma":0.002065456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1501691,"about_ca_topic_score_gemma":0.51222,"domain_scores_codex":[0.9982748,0.0007671587,0.00006790704,0.0002245028,0.0002864501,0.0003791935],"domain_scores_gemma":[0.9960271,0.001879012,0.0007958604,0.0003662087,0.0004774386,0.0004543871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001128873,0.0003659966,0.6723122,0.0003615991,0.0001143661,0.0495677,0.2341749,0.0004257052,0.004586817,0.0007084878,0.002856913,0.03441256],"study_design_scores_gemma":[0.00001336915,0.0002524178,0.6579598,0.0003460285,0.00008546033,0.01960907,0.3060835,0.001171511,0.001033265,0.0004108775,0.0129796,0.00005509362],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974402,0.0002276119,0.0006509375,0.0004428849,0.000009236356,0.0000419804,0.0001747343,0.000008051823,0.001004213],"genre_scores_gemma":[0.9952833,0.0003621607,0.003270081,0.0001804366,0.00001625229,0.0000417846,0.0001895531,0.00001341298,0.0006430197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1501691,"threshold_uncertainty_score":0.2985901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4411572768937754,"score_gpt":0.3359081729101964,"score_spread":0.105249103983579,"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."}}