{"id":"W7055836997","doi":"","title":"Eco-evolutionary rescue: an adaptive dynamic analysis","year":2012,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Thermal properties of materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Trait; Population; Intraspecific competition; Selection (genetic algorithm); Extinction (optical mineralogy); Expression (computer science); Interspecific competition; Adaptive value; Range (aeronautics)","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002193036,0.001246845,0.00164564,0.0008235723,0.001607651,0.0002440152,0.001918251,0.001274491,0.01136708],"category_scores_gemma":[0.0004392542,0.001221102,0.0007136699,0.0007413579,0.0001796409,0.002943909,0.0003586599,0.001047747,0.006221836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430752,"about_ca_system_score_gemma":0.0001121977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785039,"about_ca_topic_score_gemma":0.003668934,"domain_scores_codex":[0.9921675,0.001467142,0.001527422,0.001890508,0.00142511,0.001522357],"domain_scores_gemma":[0.99554,0.0001629758,0.001011717,0.001911325,0.0006601068,0.0007139113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001378766,0.0003744057,0.00001571798,0.0001853844,0.0006410659,0.00003925312,0.00002280245,0.0003582317,0.9826676,0.005173259,0.000005446528,0.009138052],"study_design_scores_gemma":[0.001778898,0.001044508,0.04008885,0.0006091372,0.007083113,0.0000677762,0.001507111,0.0002621995,0.9152424,0.008023826,0.01839033,0.005901795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9715878,0.0008556054,4.132165e-7,0.000009395183,0.003300961,0.0008271293,0.005302052,0.0006571309,0.01745953],"genre_scores_gemma":[0.9836726,0.0000900216,0.00170545,0.0001225626,0.0001558676,0.0002524101,0.003571564,0.0003312999,0.01009826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06742515,"threshold_uncertainty_score":0.9996921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934070005114073,"score_gpt":0.2512406155808007,"score_spread":0.23189991552966,"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."}}