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Enregistrement W2783734426 · doi:10.1074/mcp.ra117.000574

Cross-species Comparison of Proteome Turnover Kinetics

2018· article· en· W2783734426 sur OpenAlexaff
Kyle Swovick, Kevin Welle, Jennifer R. Hryhorenko, Andrei Seluanov, Vera Gorbunova, Sina Ghaemmaghami

Notice bibliographique

RevueMolecular & Cellular Proteomics · 2018
Typearticle
Langueen
DomaineChemistry
ThématiqueAdvanced Proteomics Techniques and Applications
Établissements canadiensIONICS Mass Spectrometry (Canada)
Organismes subventionnairesNational Institute of General Medical SciencesNational Institute on AgingNational Institutes of HealthNational Science Foundation
Mots-clésProteomeProtein turnoverKineticsProteomicsBiologyBiochemistryComputational biologyCell biologyChemistryProtein biosynthesisGene

Résumé

récupéré en direct d'OpenAlex

The constitutive process of protein turnover plays a key role in maintaining cellular homeostasis. Recent technological advances in mass spectrometry have enabled the measurement of protein turnover kinetics across the proteome. However, it is not known if turnover kinetics of individual proteins are highly conserved or if they have evolved to meet the physiological demands of individual species. Here, we conducted systematic analyses of proteome turnover kinetics in primary dermal fibroblasts isolated from eight different rodent species. Our results highlighted two trends in the variability of proteome turnover kinetics across species. First, we observed a decrease in cross-species correlation of protein degradation rates as a function of evolutionary distance. Second, we observed a negative correlation between global protein turnover rates and maximum lifespan of the species. We propose that by reducing the energetic demands of continuous protein turnover, long-lived species may have evolved to lessen the generation of reactive oxygen species and the corresponding oxidative damage over their extended lifespans. The constitutive process of protein turnover plays a key role in maintaining cellular homeostasis. Recent technological advances in mass spectrometry have enabled the measurement of protein turnover kinetics across the proteome. However, it is not known if turnover kinetics of individual proteins are highly conserved or if they have evolved to meet the physiological demands of individual species. Here, we conducted systematic analyses of proteome turnover kinetics in primary dermal fibroblasts isolated from eight different rodent species. Our results highlighted two trends in the variability of proteome turnover kinetics across species. First, we observed a decrease in cross-species correlation of protein degradation rates as a function of evolutionary distance. Second, we observed a negative correlation between global protein turnover rates and maximum lifespan of the species. We propose that by reducing the energetic demands of continuous protein turnover, long-lived species may have evolved to lessen the generation of reactive oxygen species and the corresponding oxidative damage over their extended lifespans. Within a cell, proteins are in a state of flux and are continually degraded and re-synthesized (1.Goldberg A.L. St John A.C. Intracellular protein degradation in mammalian and bacterial cells: Part 2.Ann. Rev. Biochem. 1976; 45: 747-803Crossref PubMed Scopus (808) Google Scholar). The process of protein turnover plays a critical quality control function in cells. Over time, proteins tend to become damaged by a number of stochastic mechanisms including oxidation, nitrosylation, and aggregation (2.Stadtman E.R. Levine R.L. Free radical-mediated oxidation of free amino acids and amino acid residues in proteins.Amino Acids. 2003; 25: 207-218Crossref PubMed Scopus (1390) Google Scholar). The process of turnover ensures that damaged proteins are perpetually replaced by a nascent pool of undamaged, functional proteins. Additionally, protein turnover plays an important role in the regulation of protein expression levels. The constant turnover of proteins allows their steady-state levels to adjust in response to changes in synthesis rates (3.Davies K.J. Protein damage and degradation by oxygen radicals. I. general aspects.J. Biol. Chem. 1987; 262: 9895-9901Abstract Full Text PDF PubMed Google Scholar, 4.Ryazanov A.G. Nefsky B.S. Protein turnover plays a key role in aging.Mech. Ageing Development. 2002; 123: 207-213Crossref PubMed Scopus (79) Google Scholar). Recent advances in quantitative proteomics and mass spectrometry have enabled the measurement of protein turnover kinetics on proteome-wide scales (5.Pratt J.M. Petty J. Riba-Garcia I. Robertson D.H. Gaskell S.J. Oliver S.G. Beynon R.J. Dynamics of protein turnover, a missing dimension in proteomics.Mol. Cell. Proteomics. 2002; 1: 579-591Abstract Full Text Full Text PDF PubMed Scopus (329) Google Scholar, 6.Price J.C. Guan S. Burlingame A. Prusiner S.B. Ghaemmaghami S. Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 14508-14513Crossref PubMed Scopus (247) Google Scholar, 7.Claydon A.J. Beynon R. Proteome dynamics: revisiting turnover with a global perspective.Mol. Cell. Proteomics. 2012; 11: 1551-1565Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar, 8.Cambridge S.B. Gnad F. Nguyen C. Bermejo J.L. Kruger M. Mann M. Systems-wide proteomic analysis in mammalian cells reveals conserved, functional protein turnover.J. Proteome Res. 2011; 10: 5275-5284Crossref PubMed Scopus (177) Google Scholar, 9.Schwanhausser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4058) Google Scholar, 10.Toyama B.H. Savas J.N. Park S.K. Harris M.S. Ingolia N.T. Yates 3rd, J.R. Hetzer M.W. Identification of long-lived proteins reveals exceptional stability of essential cellular structures.Cell. 2013; 154: 971-982Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar). These studies have shown that turnover rates are highly variable within the proteome, with protein half-lives ranging from minutes to years. Several factors can influence the turnover rates of proteins in vivo. In some proteins, identities of N-terminal residues appear to have a strong influence on half-lives, a phenomenon referred to as the “N-end rule” (11.Bachmair A. Finley D. Varshavsky A. In vivo half-life of a protein is a function of its amino-terminal residue.Science. 1986; 234: 179-186Crossref PubMed Scopus (1372) Google Scholar). The presence of longer sequence domains, termed “degrons,” have also been shown to affect protein turnover. For example, sequences rich in proline, glutamic acid, serine, and threonine have been shown to act as robust degradative markers (12.Rogers S. Wells R. Rechsteiner M. Amino acid sequences common to rapidly degraded proteins: the PEST hypothesis.Science. 1986; 234: 364Crossref PubMed Scopus (1957) Google Scholar, 13.Reverte C.G. Ahearn M.D. Hake L.E. CPEB degradation during Xenopus oocyte maturation requires a PEST domain and the 26S proteasome.Developmental Biol. 2001; 231: 447-458Crossref PubMed Scopus (76) Google Scholar). In addition to sequence determinants, physical properties of proteins such as isoelectric points, surface areas, thermodynamic stabilities and molecular weights can influence half-lives (14.Dice J.F. Hess E.J. Goldberg A.L. Studies on the relationship between the degradative rates of proteins in vivo and their isoelectric points.Biochem. J. 1979; 178: 305-312Crossref PubMed Scopus (30) Google Scholar, 15.Dice J.F. Goldberg A.L. Relationship between in vivo degradative rates and isoelectric points of proteins.Proc. Natl. Acad. Sci. U.S.A. 1975; 72: 3893-3897Crossref PubMed Scopus (129) Google Scholar, 16.Miller S. Lesk A.M. Janin J. Chothia C. The accessible surface area and stability of oligomeric proteins.Nature. 1987; 328: 834-836Crossref PubMed Scopus (307) Google Scholar). However, none of these determinants are universally applicable to the entirety of the proteome and it is currently not possible to predict the half-life of a protein based solely on its sequence and structure. The turnover rate of a protein is not only dependent on its sequence and structure, but also on the relative activity and selectivity of proteolytic pathways within the cell. Hence, proteins of identical sequence can have vastly different half-lives within different cell types, tissues and environmental conditions (6.Price J.C. Guan S. Burlingame A. Prusiner S.B. Ghaemmaghami S. Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 14508-14513Crossref PubMed Scopus (247) Google Scholar, 7.Claydon A.J. Beynon R. Proteome dynamics: revisiting turnover with a global perspective.Mol. Cell. Proteomics. 2012; 11: 1551-1565Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar). Within a cell, proteins can be degraded by several proteolytic pathways and proteases. In eukaryotes, the two major degradation pathways with broad selectivity are autophagy and the ubiquitin proteasome system (UPS) 1The abbreviations used are: UPS, ubiquitin proteasome system; AGC, automatic gain control; CID, collision induced dissociation; SILAC, stable isotopic labeling in cell culture; CV, coefficient of variation; LC-MS/MS, liquid chromatography tandem mass spectrometry; PRIDE, proteomics identifications; rs, Spearman's rank correlation coefficient; r, Pearson's correlation coefficient; GO, gene ontology; mRNA, messenger ribonucleic acid; ROS, reactive oxygen species; DTT, dithiothreitol; FBS, fetal bovine serum; EMEM, Eagle's minimum essential medium; BCA, bicinchoninic assay; PBS, phosphate buffer saline; MEM, minimum essential medium; TCEP, Tris(2-carboxyethyl)phosphine; IAA, iodoacetamide; RT, room temperature; TFA, trifluoric acid; mTOR, mammalian target of rapamycin; IGF-1, insulin growth factor-1; ATP, adenosine triphosphate; DNA, deoxyribonucleic acid; REVIGO, reduce and visualize gene ontology; PSM, peptide spectral match. 1The abbreviations used are: UPS, ubiquitin proteasome system; AGC, automatic gain control; CID, collision induced dissociation; SILAC, stable isotopic labeling in cell culture; CV, coefficient of variation; LC-MS/MS, liquid chromatography tandem mass spectrometry; PRIDE, proteomics identifications; rs, Spearman's rank correlation coefficient; r, Pearson's correlation coefficient; GO, gene ontology; mRNA, messenger ribonucleic acid; ROS, reactive oxygen species; DTT, dithiothreitol; FBS, fetal bovine serum; EMEM, Eagle's minimum essential medium; BCA, bicinchoninic assay; PBS, phosphate buffer saline; MEM, minimum essential medium; TCEP, Tris(2-carboxyethyl)phosphine; IAA, iodoacetamide; RT, room temperature; TFA, trifluoric acid; mTOR, mammalian target of rapamycin; IGF-1, insulin growth factor-1; ATP, adenosine triphosphate; DNA, deoxyribonucleic acid; REVIGO, reduce and visualize gene ontology; PSM, peptide spectral match. (17.Klionsky D.J. Emr S.D. Autophagy as a Regulated Pathway of Cellular Degradation.Science. 2000; 290: 1717Crossref PubMed Scopus (2969) Google Scholar). These two pathways are believed to have distinct functions in maintaining protein homeostasis. Autophagy has been shown to be in the degradation of damaged protein long-lived proteins, of during of and protein In the is believed to be the degradation of and proteins and of damaged proteins D. D.J. The of Res. PubMed Scopus Google Scholar, D. I. A. The role of protein mechanisms in and PubMed Scopus Google Scholar). the primary mechanisms of the relative of autophagy and can a major role in the half-lives of proteins in vivo. are the turnover kinetics of individual proteins conserved across a proteomic studies have in the results have been For example, a of and observed in protein turnover rates between the two species R. N. F. Global proteome turnover analyses of the S. and S. Full Text Full Text PDF PubMed Scopus Google Scholar). an analysis of two cell and from and mouse tissues a correlation in protein turnover rates S.B. Gnad F. Nguyen C. Bermejo J.L. Kruger M. Mann M. Systems-wide proteomic analysis in mammalian cells reveals conserved, functional protein turnover.J. Proteome Res. 2011; 10: 5275-5284Crossref PubMed Scopus (177) Google Scholar). In a of in vivo turnover rates in two mouse and in two also correlation A.J. D. J.L. Beynon R.J. Proteome dynamics: in the kinetics of in Cell. Proteomics. Full Text Full Text PDF PubMed Scopus Google Scholar). However, to a systematic cross-species of protein turnover rates a of has not been conducted in a Here, we have used isotopic labeling and quantitative proteomics to protein turnover kinetics in primary dermal fibroblasts isolated from eight different rodent species. The species to a of evolutionary and physiological including and The results a systematic of proteome turnover kinetics in a cell across species. The the is in the For of dermal fibroblasts isolated from different to and and in The labeling of the the the In a the with mouse cells in to the The of the rate labeling conducted by analysis of the to from the and the of by The of rate the protein by the coefficient of The correlation of rates between species by For rank correlation used as the degradation rates not dermal fibroblasts isolated and to the by A. A. primary from 2010; Scholar, A. C. J. M. M. to a to of Natl. Acad. Sci. U.S.A. PubMed Scopus Google Scholar). The isolated fibroblasts in with fetal bovine and and isotopic to The from that of the phenomenon of A. C. J. M. M. to a to of Natl. Acad. Sci. U.S.A. PubMed Scopus Google Scholar). cells cell of they in a state the cells to the labeling with and in the to with and of and FBS, and and of cells with PBS, and the of conducted mouse cells from two in a buffer and in of buffer cells and to a with on The and the to Protein by the bicinchoninic of protein from of with and protein with in to to and to a of with of on the of and and the acid to a of proteome conducted on the different in with and the with and acid the and some of the different in with and the with and the and to the eight in and in of with an system to a mass For the acid in used as and acid in used as B. For that with the and to over to over to over The to over to the For that with used For and the from over For and the from over For and the from in and and the from in For the to the peptide and and to over and to the The The in with a by The over a of with a of of an target of and a maximum of The with an target of and a maximum of The with an of and a collision of the M. R. M. C. or the with J. Mann M. peptide mass and proteome-wide protein PubMed Scopus Google Scholar). The maximum of in two and the to a maximum of peptide and protein quantification with the in For to by a to isotopic within that corresponding peptide spectral J. Mann M. peptide mass and proteome-wide protein PubMed Scopus Google Scholar). The to The of degradation rate from conducted in to the in the of as a function of to a function the protein and to proteins in and to a function the to the protein to two quality control two peptide sequences in or and peptide in two or The in has been (6.Price J.C. Guan S. Burlingame A. Prusiner S.B. Ghaemmaghami S. Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 14508-14513Crossref PubMed Scopus (247) Google Scholar, S. J. Ghaemmaghami S. Global Analysis of Cellular Protein the of Full Text Full Text PDF PubMed Scopus Google Scholar, C. A. J.R. Ghaemmaghami S. of degradation dynamics in response to growth Natl. Acad. Sci. U.S.A. PubMed Scopus Google and is based on the Protein synthesis is a process with to protein degradation a constant rate that is the protein protein degradation can be as a process with to protein protein of cell not during the and the system is on these we can the rate of proteins and of proteins: is the rate constant protein is the rate constant protein degradation and is the rate constant cell We the and the protein labeling are with to steady-state protein the observed labeling is conducted in the rate of cell is Hence, and a rate the relationship between and half-life of a protein is as a global cross-species of protein turnover fibroblasts isolated from eight different rodent and A. A. primary from 2010; Scholar, A. from to PubMed Scopus Google The species to a of including evolutionary and The to distinct within the and and are by of in The lifespan and mass from mass lifespan in a The is in to a the analyses in a we that cellular not to the kinetics of isotopic In isotopic labeling are conducted in the rate of labeling is the of protein degradation and cellular rates A.J. Beynon R. Proteome dynamics: revisiting turnover with a global perspective.Mol. Cell. Proteomics. 2012; 11: 1551-1565Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar). in the kinetics of protein degradation can not be proteins degradation is the of the cell N. I. A. A. Proteome Dynamics in 2011; PubMed Scopus Google Scholar). Here, by maintaining cells in a state the of we to the turnover kinetics of long-lived proteins half-lives are longer the rate of cellular The labeling of and of The in the of the The kinetics of labeling to a and the rate degradation and the corresponding half-lives In the the and be used to the protein measurement of its turnover we observed that to the protein turnover rates is by the that the of of peptide within a protein is in to the of peptide within the proteome the protein to a protein and with a the of in the we mouse cells in two The results a of correlation the of the proteomic the we turnover rates in fibroblasts isolated from the eight rodent species For species with and the Additionally, in to analysis to sequence not we the from rodent species the mouse The the of proteomic However, from the sequence in of protein of protein of proteomic For a the mouse or the as the number of peptide spectral the number of peptide the number of protein The number of Protein is to to be by two peptide The number of Protein is to two distinct be in two or on two to proteins two peptide sequences two or on two to proteins two peptide sequences two or in a The of the proteomic are in For analysis of we only proteins two or two or The results and are in and the are in the The and of by and mouse are shown in and The rates of from to corresponding to half-lives of to For of the rodent species and the be mouse and For these the mouse the protein by but not the of of and We that the of the mouse to from rodent species not the observed global of be if the highly conserved is the of proteins in these species not only highly conserved two of identical The that are in some of the rodent species. if these to in the of the peptide sequences in we the analysis to peptide sequences that species The results that in are the analysis is to species. We that may be two general mechanisms variability in protein turnover First, protein may have in sequence during the of their relative to cellular degradation Second, the relative of cellular degradation pathways autophagy and may be variable cells from different species. between these two we a of conserved proteins with identical amino acid sequences of the species with protein sequence and The that the in observed the proteome is also conserved proteins The results that systematic in turnover kinetics species are by in of the cellular degradation in sequences of protein We degradation rates within the proteome of species and the correlation of these to several properties including mass and lifespan The that the correlation to the degradation rate the lifespan of the we observed a strong negative correlation between these two that long-lived rates of degradation in to the of the of J. and the Scopus Google the correlation between degradation rate and the lifespan a of The results that lifespan and protein turnover kinetics are We correlation and variability in relative by of protein species. of protein turnover kinetics between mouse and species in the and between mouse and species in different The used to in turnover For we used the Spearman's correlation of correlation as not and some in the with example, The analysis that the correlation in protein turnover rates between mouse and is the correlation between mouse and and in the in addition to a systematic a decrease in correlation of turnover of stochastic variability in relative degradation rates between the proteome of the two species. The of of or mouse and not the analysis only in species and We conducted of rodent species For we used by the mouse and to peptide sequences between of species. Analysis of that the correlation turnover rates decrease as a function of evolutionary distance. is by the that species with evolutionary are species with longer evolutionary For of the with the are to the correlation that of the observed variability can be by of the with are the results that relative protein turnover kinetics have over the of We observed that the conserved proteins are the proteome the conserved proteins not as a function of evolutionary distance. These are with the that variability in relative turnover rates within of species are to changes in sequences of protein We is a relationship between protein function and of turnover kinetics across species. We relative to the within species and the coefficient of of relative turnover rates proteins across the eight the of across the proteome. We used the R. I. D. a and of in gene 10: PubMed Scopus Google to gene in the of proteins with the with the highly conserved the the proteasome and the These are known to be the essential and highly conserved proteins in the cell A. and of mammalian Biol. PubMed Scopus Google Scholar, A. A. F. G. J.M. F. W. of the 26S proteasome a of Natl. Acad. Sci. U.S.A. PubMed Scopus Google Scholar, R. and in Biol. 2011; PubMed Scopus Google Scholar, Identification and of essential in the PubMed Scopus Google Scholar). The results that of turnover rates may be critical to of function in some proteins. has been known that within a cell, proteins have a of turnover However, the functional of a half-life has been to The of degradation rates within the cell are that influence protein levels such as stabilities and and as a role in the steady-state of proteins B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4058) Google Scholar). studies of in protein half-lives species S.B. Gnad F. Nguyen C. Bermejo J.L. Kruger M. Mann M. Systems-wide proteomic analysis in mammalian cells reveals conserved, functional protein turnover.J. Proteome Res. 2011; 10: 5275-5284Crossref PubMed Scopus (177) Google Scholar, R. N. F. Global proteome turnover analyses of the S. and S. Full Text Full Text PDF PubMed Scopus Google Scholar, A.J. D. J.L. Beynon R.J. Proteome dynamics: in the kinetics of in Cell. Proteomics. Full Text Full Text PDF PubMed Scopus Google Scholar). However, these studies based on of from cell or In we a systematic of proteome turnover rates within a cell across species and their The on the and functional of global protein turnover Our results that turnover kinetics are highly rodent species the of species and the variability in are by We observed that decrease as a function of evolutionary distance. However, not observed the of proteins sequences conserved that in relative turnover rates of proteins are to changes in their amino acid sequence over the of The may be in two is known that sequence act as a Additionally, changes in amino acid sequence may physical properties of proteins thermodynamic stability or that the of the protein to changes in sequences of target proteins may their turnover it can be that proteins with conserved sequences are to be the critical proteins in the cell. in their turnover kinetics may be to the selectivity of the degradation in a cell may have evolved to a degradation rate these proteins during the of In the in sequence changes in degradation in the of sequence and turnover kinetics with and are not Our between these two In addition to in correlation between of we observed systematic in the of some species. we observed a strong negative correlation between and of that long-lived species rates of degradation in to species. correlation in of studies that protein turnover is G. M. M. B. of on proteasome and function in J. Biochem. Biol. 2003; PubMed Scopus Google Scholar, N. D. E.J. Chen D. D. proteome turnover and by or the Cell. PubMed Scopus Google Scholar, B. A. R.J. Li C. J. analysis reveals mechanisms autophagy in and in Natl. Acad. Sci. U.S.A. 2010; 107: PubMed Scopus Google Scholar). in protein turnover has been to the process of in several different For example, it has been observed that as of the and autophagy pathways of has been highlighted as of the of C. M. G. The of 2013; Full Text Full Text PDF PubMed Scopus Google Scholar). Additionally, several such as and are by protein aggregation and the of that results from the of stable in the cell. several and pathways with including mTOR, and are known to be of autophagy or A. of an molecular in Natl. Acad. Sci. U.S.A. PubMed Scopus Google Scholar, C. G. G. Autophagy and 2011; Full Text Full Text PDF PubMed Scopus Google Scholar, J. A. M. S. Goldberg A.L. protein degradation by the and pathways in Full Text Full Text PDF PubMed Scopus Google Scholar). these have to the that the of robust protein turnover is an important However, it is important to that protein turnover is of the in Protein synthesis and protein degradation pathways the of constitutive protein turnover as as of the in the R. of and 2000; PubMed Scopus (177) Google Scholar). of the in the is the process of oxidative However, a of oxidative is the stochastic generation of reactive oxygen species is known that of results in oxidative damage to a number of including proteins, and and is with several D. a based on free and 11: PubMed Scopus Google Scholar). the of proteins by constitutive turnover is to the cell, its to the generation of may its to the is possible that constitutive protein turnover is a process in in long-lived damage can in tissues over an extended its are by its of turnover of the protein long-lived may be on degradative mechanisms and of damaged proteins. a the of protein degradation its energetic long-lived may have evolved robust quality control mechanisms in to the of protein damage and the turnover In of it has been shown that longer a of J. Chen A. has with the as as a Natl. Acad. Sci. U.S.A. 2013; PubMed Scopus Google Scholar, R. A. D. G. D. J. and the and of Res. 2013; PubMed Scopus Google Scholar). the functional of turnover rates in long-lived and are the number We the of the and their and with

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,153
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,297
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

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Citations58
Publié2018
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