{"id":"W6999715546","doi":"","title":"Développements de stratégies de spectrométrie de masse sur tissu pour l’identification, la quantification et la cartographie","year":2014,"lang":"fr","type":"article","venue":"theses.fr (ABES)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut Universitaire de France; Ministère de l'Enseignement Supérieur et de la Recherche; Ligue Contre le Cancer; Université de Lille; Université de Sherbrooke","keywords":"Accelerated solvent extraction; Fluorescent labelling; Fish <Actinopterygii>","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001958019,0.000403272,0.0003363354,0.0001333208,0.0002882099,0.0003265709,0.0007483517,0.0004770489,0.0002790984],"category_scores_gemma":[0.0004508746,0.0004858866,0.0001755323,0.0004093938,0.0003329877,0.0002836485,0.0001203688,0.0006395627,0.000122116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000315233,"about_ca_system_score_gemma":0.000347689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103741,"about_ca_topic_score_gemma":0.0001472689,"domain_scores_codex":[0.9972298,0.0004356298,0.0006380542,0.0006398149,0.0002886139,0.0007680785],"domain_scores_gemma":[0.9972399,0.0006793527,0.0004533564,0.001160311,0.000218063,0.000249017],"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":[0.00001895613,0.0004056269,0.006501993,0.0002378521,0.00008310841,0.000008544291,0.001064703,0.0001924016,0.5262558,0.4564183,0.001089274,0.007723409],"study_design_scores_gemma":[0.0004652392,0.00002495343,0.005577332,0.000232834,0.0001655788,0.00009663932,0.0008552399,0.001769273,0.6699399,0.1559889,0.1643329,0.0005512965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3859438,0.0009097633,0.5599232,0.00802156,0.00008301769,0.0006093636,0.0002654808,0.0005459639,0.04369783],"genre_scores_gemma":[0.9105626,0.004042416,0.08172348,0.0001396958,0.0002316458,0.000389969,0.0002018125,0.0001080848,0.002600304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5246188,"threshold_uncertainty_score":0.9997593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916667130707677,"score_gpt":0.3168697313496294,"score_spread":0.2977030600425526,"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."}}