{"id":"W4308708771","doi":"10.1371/journal.pbio.3001843","title":"The benefits of contributing to the citizen science platform iNaturalist as an identifier","year":2022,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Wildlife Federation","funders":"","keywords":"Identifier; Citizen science; Biology; Biodiversity; Data science; Value (mathematics); Unique identifier; Computational biology; Environmental resource management; Ecology; Computer science","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":[0.1261938,0.00155011,0.002121529,0.005154806,0.005373855,0.01402078,0.004205496,0.005851688,0.02198226],"category_scores_gemma":[0.2788091,0.001438105,0.001730568,0.005128948,0.003983905,0.03350079,0.04429244,0.007112264,0.02635094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001900799,"about_ca_system_score_gemma":0.00860516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003687366,"about_ca_topic_score_gemma":0.003224419,"domain_scores_codex":[0.8986712,0.05854622,0.009136892,0.009743695,0.01932926,0.004572831],"domain_scores_gemma":[0.5083477,0.1550636,0.03987172,0.1981866,0.06996641,0.02856396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.003495722,0.0005140988,0.06780246,0.002757147,0.0003606617,0.001864453,0.01923141,0.002405726,0.01497403,0.1054169,0.4185793,0.362598],"study_design_scores_gemma":[0.0001697091,0.0002851421,0.01285318,0.0007727348,0.0001298393,0.0007536095,0.004057348,0.004630746,0.003371866,0.03472935,0.9379472,0.0002993726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0847232,0.00285662,0.323349,0.1763318,0.02280441,0.003961441,0.03032663,0.06856657,0.2870802],"genre_scores_gemma":[0.3448693,0.002005938,0.4898535,0.02829691,0.01079624,0.004932666,0.03326491,0.01774442,0.06823602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1261938,"threshold_uncertainty_score":0.6673844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484342739119578,"score_gpt":0.2785413713923099,"score_spread":0.2436979440011142,"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."}}