{"id":"W4415464059","doi":"10.1242/jeb.251676","title":"ECR Spotlight – Mellissa Easwaramoorthy","year":2025,"lang":"en","type":"article","venue":"Journal of Experimental Biology","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Selection (genetic algorithm); Diversity (politics); Experimental biology","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":[0.005421821,0.0007488646,0.0005395246,0.0009721993,0.001949877,0.004110245,0.001148978,0.002854979,0.08563063],"category_scores_gemma":[0.01337368,0.0003587014,0.0003580178,0.0005030308,0.00107473,0.002866602,0.002545605,0.005218546,0.04442632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002094133,"about_ca_system_score_gemma":0.004735677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003070672,"about_ca_topic_score_gemma":0.007074097,"domain_scores_codex":[0.9960807,0.0005938514,0.0001399211,0.0007671037,0.002044799,0.0003736244],"domain_scores_gemma":[0.9778258,0.00191801,0.0005607433,0.000682923,0.008971043,0.0100415],"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":[0.00002719587,0.00001425581,0.00006979967,0.00002766511,0.000001381082,0.0001109384,0.00008379234,0.00001387346,0.0004312929,0.0005604035,0.9884251,0.01023428],"study_design_scores_gemma":[0.000006618418,0.00002095445,0.0002570167,0.0000680302,0.000001002534,0.0002765469,0.0002636253,0.00003921566,0.0002588273,0.0003162372,0.9984794,0.00001251308],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.004717968,0.02566158,0.003850405,0.6294871,0.2105273,0.0002036541,0.001144362,0.002365633,0.122042],"genre_scores_gemma":[0.02325299,0.01070755,0.003061422,0.1560542,0.03287793,0.0001952962,0.000610364,0.001650545,0.7715898],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08563063,"threshold_uncertainty_score":0.286463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293400030554316,"score_gpt":0.2745052784006968,"score_spread":0.2615712780951536,"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."}}