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Enregistrement W2118550160 · doi:10.1074/mcp.m110.002113

Dynamics of the Skeletal Muscle Secretome during Myoblast Differentiation

2010· article· en· W2118550160 sur OpenAlex

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Notice bibliographique

RevueMolecular & Cellular Proteomics · 2010
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMuscle Physiology and Disorders
Établissements canadiensnon disponible
Organismes subventionnairesNovo Nordisk FondenH. Lundbeck A/SLundbeckfondenSundhed og Sygdom, Det Frie ForskningsrådDanmarks GrundforskningsfondNational Research FoundationMedical Research CouncilNovo Nordisk
Mots-clésMyogenesisSkeletal muscleSecretory proteinStable isotope labeling by amino acids in cell cultureCell biologySecretionC2C12BiologyParacrine signallingMyocyteProteomicsBiochemistryEndocrinologyReceptorGene

Résumé

récupéré en direct d'OpenAlex

During recent years, increased efforts have focused on elucidating the secretory function of skeletal muscle. Through secreted molecules, skeletal muscle affects local muscle biology in an auto/paracrine manner as well as having systemic effects on other tissues. Here we used a quantitative proteomics platform to investigate the factors secreted during the differentiation of murine C2C12 skeletal muscle cells. Using triple encoding stable isotope labeling by amino acids in cell culture, we compared the secretomes at three different time points of muscle differentiation and followed the dynamics of protein secretion. We identified and quantitatively analyzed 635 secreted proteins, including 35 growth factors, 40 cytokines, and 36 metallopeptidases. The extensive presence of these proteins that can act as potent signaling mediators to other cells and tissues strongly highlights the important role of the skeletal muscle as a prominent secretory organ. In addition to previously reported molecules, we identified many secreted proteins that have not previously been shown to be released from skeletal muscle cells nor shown to be differentially released during the process of myogenesis. We found 188 of these secreted proteins to be significantly regulated during the process of myogenesis. Comparative analyses of selected secreted proteins revealed little correlation between their mRNA and protein levels, indicating pronounced regulation by posttranscriptional mechanisms. Furthermore, analyses of the intracellular levels of members of the semaphorin family and their corresponding secretion dynamics demonstrated that the release of secreted proteins is tightly regulated by the secretory pathway, the stability of the protein, and/or the processing of secreted proteins. Finally, we provide 299 unique hydroxyproline sites mapping to 48 distinct secreted proteins and have discovered a novel hydroxyproline motif. During recent years, increased efforts have focused on elucidating the secretory function of skeletal muscle. Through secreted molecules, skeletal muscle affects local muscle biology in an auto/paracrine manner as well as having systemic effects on other tissues. Here we used a quantitative proteomics platform to investigate the factors secreted during the differentiation of murine C2C12 skeletal muscle cells. Using triple encoding stable isotope labeling by amino acids in cell culture, we compared the secretomes at three different time points of muscle differentiation and followed the dynamics of protein secretion. We identified and quantitatively analyzed 635 secreted proteins, including 35 growth factors, 40 cytokines, and 36 metallopeptidases. The extensive presence of these proteins that can act as potent signaling mediators to other cells and tissues strongly highlights the important role of the skeletal muscle as a prominent secretory organ. In addition to previously reported molecules, we identified many secreted proteins that have not previously been shown to be released from skeletal muscle cells nor shown to be differentially released during the process of myogenesis. We found 188 of these secreted proteins to be significantly regulated during the process of myogenesis. Comparative analyses of selected secreted proteins revealed little correlation between their mRNA and protein levels, indicating pronounced regulation by posttranscriptional mechanisms. Furthermore, analyses of the intracellular levels of members of the semaphorin family and their corresponding secretion dynamics demonstrated that the release of secreted proteins is tightly regulated by the secretory pathway, the stability of the protein, and/or the processing of secreted proteins. Finally, we provide 299 unique hydroxyproline sites mapping to 48 distinct secreted proteins and have discovered a novel hydroxyproline motif. The skeletal muscle is a highly dynamic organ responsible for locomotion and generation of body heat and plays an essential role in the maintenance of metabolic homeostasis. Furthermore, it is the major target for insulin-induced glucose uptake and has a high capacity to metabolize fatty acids. Impaired glucose metabolism and lipid metabolism in the skeletal muscle are characteristic hallmarks of insulin resistance associated with different diseases such as obesity, type 2 diabetes, and metabolic syndrome (1Pedersen B.K. The diseasome of physical inactivity—and the role of myokines in muscle-fat cross talk.J. Physiol. 2009; 587: 5559-5568Crossref PubMed Scopus (419) Google Scholar). During the last decade, data have emerged demonstrating that the skeletal muscle also plays an active role as an endocrine organ. Skeletal muscles have been suggested to be a source of secreted proteins, conceptualized as myokines that can influence metabolism and other biological processes in a systemic manner at different tissue targets. Regular exercise has many beneficial effects on whole body well-being: it improves insulin sensitivity, metabolic processes, and blood pressure and reduces inflammation mediated through alterations of gene and protein expression in different cell types. Several studies have described the association of physical inactivity with the development of different pathologies, including mental diseases as well as obesity, type 2 diabetes, metabolic syndrome, and cardiovascular diseases (1Pedersen B.K. The diseasome of physical inactivity—and the role of myokines in muscle-fat cross talk.J. Physiol. 2009; 587: 5559-5568Crossref PubMed Scopus (419) Google Scholar). It has been suggested that the skeletal muscle, through secreted molecules, could be transmitting many of the beneficial effects of physical activity by affecting whole body homeostasis in an autocrine, paracrine, and/or endocrine fashion.The initial recognition of skeletal muscle as an endocrine organ originates from studies that identified interleukin (IL)-6 expression and secretion by contracting skeletal muscle. The plasma level of muscle-derived IL-6 increases in response to physical activity, and the secreted IL-6 can affect metabolic and inflammatory processes (2Steensberg A. van Hall G. Osada T. Sacchetti M. Saltin B. Klarlund Pedersen B. Production of interleukin-6 in contracting human skeletal muscles can account for the exercise-induced increase in plasma interleukin-6.J. Physiol. 2000; 529: 237-242Crossref PubMed Scopus (717) Google Scholar, 3Pedersen B.K. Febbraio M.A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6.Physiol. Rev. 2008; 88: 1379-1406Crossref PubMed Scopus (1379) Google Scholar). In addition to IL-6, other potential myokines, with either systemic effects or local effects in an autocrine and paracrine manner, have been identified, including IL-8 and IL-15 (3Pedersen B.K. Febbraio M.A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6.Physiol. Rev. 2008; 88: 1379-1406Crossref PubMed Scopus (1379) Google Scholar). Furthermore, a mouse model of inducible muscle hypertrophy was used as a tool for the identification of new myokines, resulting in the identification of fibroblast growth factor (FGF)-21 and follistatin-like 1 as secreted skeletal muscle proteins involved in metabolic processes and vascularization (4Hojman P. Pedersen M. Nielsen A.R. Krogh-Madsen R. Yfanti C. Akerstrom T. Nielsen S. Pedersen B.K. Fibroblast growth factor-21 is induced in human skeletal muscles by hyperinsulinemia.Diabetes. 2009; 58: 2797-2801Crossref PubMed Scopus (150) Google Scholar, 5Walsh K. Adipokines, myokines and cardiovascular disease.Circ. J. 2009; 73: 13-18Crossref PubMed Scopus (131) Google Scholar).Despite recent discoveries, the secretome of skeletal muscle has not been fully characterized, and the number of identified secreted proteins is still limited. The majority of studies investigating the secretory profile of the skeletal muscle have used traditional biochemical and molecular biology strategies to identify muscle-secreted proteins; although powerful, these approaches are restricted to a few proteins. Existing mass spectrometry-driven attempts to target the muscle-specific secreted proteome resulted in a limited number of identified secreted proteins (6Chan X.C. McDermott J.C. Siu K.W. Identification of secreted proteins during skeletal muscle development.J. Proteome Res. 2007; 6: 698-710Crossref PubMed Scopus (66) Google Scholar, 7Hittel D.S. Berggren J.R. Shearer J. Boyle K. Houmard J.A. Increased secretion and expression of myostatin in skeletal muscle from extremely obese women.Diabetes. 2009; 58: 30-38Crossref PubMed Scopus (220) Google Scholar, 8Yoon J.H. Yea K. Kim J. Choi Y.S. Park S. Lee H. Lee C.S. Suh P.G. Ryu S.H. Comparative proteomic analysis of the insulin-induced L6 myotube secretome.Proteomics. 2009; 9: 51-60Crossref PubMed Scopus (62) Google Scholar). Comprehensive quantitative analyses of the muscle secretome could provide greater insight into muscle biology and the muscle-dependent cross-talk with other tissues. The fast development of quantitative mass spectrometry-based proteomics has allowed the qualitative as well as the quantitative evaluation of complex biological processes in a large scale manner (9Dengjel J. Kratchmarova I. Blagoev B. Receptor tyrosine kinase signaling: a view from quantitative proteomics.Mol. Biosyst. 2009; 5: 1112-1121Crossref PubMed Scopus (49) Google Scholar). The SILAC 1The abbreviations used are:SILACstable isotope labeling by amino acids in cell cultureCMconditioned mediadFBSdialyzed FBSECMextracellular matrixGOgene ontologyIGFinsulin-like growth factorIGFBPIGF-binding proteinIGF-1RIGF-1 receptorIPIInternational Protein IndexLTBPlatent TGF-β-binding proteinLTQlinear trap quadrupoleMMPmatrix metalloproteinaseMRFmyogenic regulatory factorQ-PCRquantitative PCRArg6l-[13C6,14N4]arginineArg10l-[13C6,15N4]arginineLys4l-[2H4]lysineLys8l-[13C6,15N2]lysineBis-Tris2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diolTBPTATA box-binding proteinSemasemaphorin. 1The abbreviations used are:SILACstable isotope labeling by amino acids in cell cultureCMconditioned mediadFBSdialyzed FBSECMextracellular matrixGOgene ontologyIGFinsulin-like growth factorIGFBPIGF-binding proteinIGF-1RIGF-1 receptorIPIInternational Protein IndexLTBPlatent TGF-β-binding proteinLTQlinear trap quadrupoleMMPmatrix metalloproteinaseMRFmyogenic regulatory factorQ-PCRquantitative PCRArg6l-[13C6,14N4]arginineArg10l-[13C6,15N4]arginineLys4l-[2H4]lysineLys8l-[13C6,15N2]lysineBis-Tris2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diolTBPTATA box-binding proteinSemasemaphorin. methodology represents a powerful quantitative tool to study the dynamics of different biological processes, including in-depth characterization of the signaling cascades involved in various types of cellular differentiation (10Kratchmarova I. Blagoev B. Haack-Sorensen M. Kassem M. Mann M. Mechanism of divergent growth factor effects in mesenchymal stem cell differentiation.Science. 2005; 308: 1472-1477Crossref PubMed Scopus (481) Google Scholar, 11Krüger M. Kratchmarova I. Blagoev B. Tseng Y.H. Kahn C.R. Mann M. Dissection of the insulin signaling pathway via quantitative phosphoproteomics.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 2451-2456Crossref PubMed Scopus (206) Google Scholar, 12Prokhorova T.A. Rigbolt K.T. Johansen P.T. Henningsen J. Kratchmarova I. Kassem M. Blagoev B. Stable isotope labeling by amino acids in cell culture (SILAC) and quantitative comparison of the membrane proteomes of self-renewing and differentiating human embryonic stem cells.Mol. Cell. Proteomics. 2009; 8: 959-970Abstract Full Text Full Text PDF PubMed Scopus (99) Google Scholar).Here we used triple encoding SILAC to identify and quantitatively evaluate the dynamics of secreted proteins during the differentiation process of the murine C2C12 skeletal muscle cell line. We identified 635 proteins that are secreted from skeletal myoblasts; to our knowledge, this is the largest data set covering the muscle secretome. Furthermore, we quantitatively profiled the secretion of 624 of these proteins during the course of skeletal muscle differentiation. Proteins previously known to be secreted from skeletal muscle were identified as well as many proteins not previously shown to be secreted by skeletal myoblasts. Based on gene ontology (GO) annotations, the identified secreted proteins are involved in various biological processes and molecular function categories, highlighting the diversity of the muscle secretome. Moreover, 188 secreted proteins were significantly and dynamically regulated during skeletal myogenesis, suggesting involvement in skeletal muscle development in either an autocrine or paracrine manner. Among the regulated secreted factors, we identified a family of proteins, the semaphorins, that are involved in muscle development. We investigated more closely the expression profiles of semaphorins at the mRNA and protein levels during the myoblast conversion. Finally, our approach resulted in the identification of 299 posttranslationally modified sites on 48 secreted proteins containing hydroxyproline residues and revealed a new motif distinct from the canonical hydroxyproline motif. To our knowledge, this also represents the largest data set of proline hydroxylation on secreted proteins. The skeletal muscle is a highly dynamic organ responsible for locomotion and generation of body heat and plays an essential role in the maintenance of metabolic homeostasis. Furthermore, it is the major target for insulin-induced glucose uptake and has a high capacity to metabolize fatty acids. Impaired glucose metabolism and lipid metabolism in the skeletal muscle are characteristic hallmarks of insulin resistance associated with different diseases such as obesity, type 2 diabetes, and metabolic syndrome (1Pedersen B.K. The diseasome of physical inactivity—and the role of myokines in muscle-fat cross talk.J. Physiol. 2009; 587: 5559-5568Crossref PubMed Scopus (419) Google Scholar). During the last decade, data have emerged demonstrating that the skeletal muscle also plays an active role as an endocrine organ. Skeletal muscles have been suggested to be a source of secreted proteins, conceptualized as myokines that can influence metabolism and other biological processes in a systemic manner at different tissue targets. Regular exercise has many beneficial effects on whole body well-being: it improves insulin sensitivity, metabolic processes, and blood pressure and reduces inflammation mediated through alterations of gene and protein expression in different cell types. Several studies have described the association of physical inactivity with the development of different pathologies, including mental diseases as well as obesity, type 2 diabetes, metabolic syndrome, and cardiovascular diseases (1Pedersen B.K. The diseasome of physical inactivity—and the role of myokines in muscle-fat cross talk.J. Physiol. 2009; 587: 5559-5568Crossref PubMed Scopus (419) Google Scholar). It has been suggested that the skeletal muscle, through secreted molecules, could be transmitting many of the beneficial effects of physical activity by affecting whole body homeostasis in an autocrine, paracrine, and/or endocrine fashion. The initial recognition of skeletal muscle as an endocrine organ originates from studies that identified interleukin (IL)-6 expression and secretion by contracting skeletal muscle. The plasma level of muscle-derived IL-6 increases in response to physical activity, and the secreted IL-6 can affect metabolic and inflammatory processes (2Steensberg A. van Hall G. Osada T. Sacchetti M. Saltin B. Klarlund Pedersen B. Production of interleukin-6 in contracting human skeletal muscles can account for the exercise-induced increase in plasma interleukin-6.J. Physiol. 2000; 529: 237-242Crossref PubMed Scopus (717) Google Scholar, 3Pedersen B.K. Febbraio M.A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6.Physiol. Rev. 2008; 88: 1379-1406Crossref PubMed Scopus (1379) Google Scholar). In addition to IL-6, other potential myokines, with either systemic effects or local effects in an autocrine and paracrine manner, have been identified, including IL-8 and IL-15 (3Pedersen B.K. Febbraio M.A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6.Physiol. Rev. 2008; 88: 1379-1406Crossref PubMed Scopus (1379) Google Scholar). Furthermore, a mouse model of inducible muscle hypertrophy was used as a tool for the identification of new myokines, resulting in the identification of fibroblast growth factor (FGF)-21 and follistatin-like 1 as secreted skeletal muscle proteins involved in metabolic processes and vascularization (4Hojman P. Pedersen M. Nielsen A.R. Krogh-Madsen R. Yfanti C. Akerstrom T. Nielsen S. Pedersen B.K. Fibroblast growth factor-21 is induced in human skeletal muscles by hyperinsulinemia.Diabetes. 2009; 58: 2797-2801Crossref PubMed Scopus (150) Google Scholar, 5Walsh K. Adipokines, myokines and cardiovascular disease.Circ. J. 2009; 73: 13-18Crossref PubMed Scopus (131) Google Scholar). Despite recent discoveries, the secretome of skeletal muscle has not been fully characterized, and the number of identified secreted proteins is still limited. The majority of studies investigating the secretory profile of the skeletal muscle have used traditional biochemical and molecular biology strategies to identify muscle-secreted proteins; although powerful, these approaches are restricted to a few proteins. Existing mass spectrometry-driven attempts to target the muscle-specific secreted proteome resulted in a limited number of identified secreted proteins (6Chan X.C. McDermott J.C. Siu K.W. Identification of secreted proteins during skeletal muscle development.J. Proteome Res. 2007; 6: 698-710Crossref PubMed Scopus (66) Google Scholar, 7Hittel D.S. Berggren J.R. Shearer J. Boyle K. Houmard J.A. Increased secretion and expression of myostatin in skeletal muscle from extremely obese women.Diabetes. 2009; 58: 30-38Crossref PubMed Scopus (220) Google Scholar, 8Yoon J.H. Yea K. Kim J. Choi Y.S. Park S. Lee H. Lee C.S. Suh P.G. Ryu S.H. Comparative proteomic analysis of the insulin-induced L6 myotube secretome.Proteomics. 2009; 9: 51-60Crossref PubMed Scopus (62) Google Scholar). Comprehensive quantitative analyses of the muscle secretome could provide greater insight into muscle biology and the muscle-dependent cross-talk with other tissues. The fast development of quantitative mass spectrometry-based proteomics has allowed the qualitative as well as the quantitative evaluation of complex biological processes in a large scale manner (9Dengjel J. Kratchmarova I. Blagoev B. Receptor tyrosine kinase signaling: a view from quantitative proteomics.Mol. Biosyst. 2009; 5: 1112-1121Crossref PubMed Scopus (49) Google Scholar). The SILAC 1The abbreviations used are:SILACstable isotope labeling by amino acids in cell cultureCMconditioned mediadFBSdialyzed FBSECMextracellular matrixGOgene ontologyIGFinsulin-like growth factorIGFBPIGF-binding proteinIGF-1RIGF-1 receptorIPIInternational Protein IndexLTBPlatent TGF-β-binding proteinLTQlinear trap quadrupoleMMPmatrix metalloproteinaseMRFmyogenic regulatory factorQ-PCRquantitative PCRArg6l-[13C6,14N4]arginineArg10l-[13C6,15N4]arginineLys4l-[2H4]lysineLys8l-[13C6,15N2]lysineBis-Tris2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diolTBPTATA box-binding proteinSemasemaphorin. 1The abbreviations used are:SILACstable isotope labeling by amino acids in cell cultureCMconditioned mediadFBSdialyzed FBSECMextracellular matrixGOgene ontologyIGFinsulin-like growth factorIGFBPIGF-binding proteinIGF-1RIGF-1 receptorIPIInternational Protein IndexLTBPlatent TGF-β-binding proteinLTQlinear trap quadrupoleMMPmatrix metalloproteinaseMRFmyogenic regulatory factorQ-PCRquantitative PCRArg6l-[13C6,14N4]arginineArg10l-[13C6,15N4]arginineLys4l-[2H4]lysineLys8l-[13C6,15N2]lysineBis-Tris2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diolTBPTATA box-binding proteinSemasemaphorin. methodology represents a powerful quantitative tool to study the dynamics of different biological processes, including in-depth characterization of the signaling cascades involved in various types of cellular differentiation (10Kratchmarova I. Blagoev B. Haack-Sorensen M. Kassem M. Mann M. Mechanism of divergent growth factor effects in mesenchymal stem cell differentiation.Science. 2005; 308: 1472-1477Crossref PubMed Scopus (481) Google Scholar, 11Krüger M. Kratchmarova I. Blagoev B. Tseng Y.H. Kahn C.R. Mann M. Dissection of the insulin signaling pathway via quantitative phosphoproteomics.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 2451-2456Crossref PubMed Scopus (206) Google Scholar, 12Prokhorova T.A. Rigbolt K.T. Johansen P.T. Henningsen J. Kratchmarova I. Kassem M. Blagoev B. Stable isotope labeling by amino acids in cell culture (SILAC) and quantitative comparison of the membrane proteomes of self-renewing and differentiating human embryonic stem cells.Mol. Cell. Proteomics. 2009; 8: 959-970Abstract Full Text Full Text PDF PubMed Scopus (99) Google Scholar). stable isotope labeling by amino acids in cell culture conditioned media dialyzed FBS extracellular matrix gene ontology insulin-like growth factor IGF-binding protein IGF-1 receptor International Protein Index latent TGF-β-binding protein linear trap quadrupole matrix metalloproteinase myogenic regulatory factor quantitative PCR l-[13C6,14N4]arginine l-[13C6,15N4]arginine l-[2H4]lysine l-[13C6,15N2]lysine 2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol TATA box-binding protein semaphorin. stable isotope labeling by amino acids in cell culture conditioned media dialyzed FBS extracellular matrix gene ontology insulin-like growth factor IGF-binding protein IGF-1 receptor International Protein Index latent TGF-β-binding protein linear trap quadrupole matrix metalloproteinase myogenic regulatory factor quantitative PCR l-[13C6,14N4]arginine l-[13C6,15N4]arginine l-[2H4]lysine l-[13C6,15N2]lysine 2-[bis(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol TATA box-binding protein semaphorin. Here we used triple encoding SILAC to identify and quantitatively evaluate the dynamics of secreted proteins during the differentiation process of the murine C2C12 skeletal muscle cell line. We identified 635 proteins that are secreted from skeletal myoblasts; to our knowledge, this is the largest data set covering the muscle secretome. Furthermore, we quantitatively profiled the secretion of 624 of these proteins during the course of skeletal muscle differentiation. Proteins previously known to be secreted from skeletal muscle were identified as well as many proteins not previously shown to be secreted by skeletal myoblasts. Based on gene ontology (GO) annotations, the identified secreted proteins are involved in various biological processes and molecular function categories, highlighting the diversity of the muscle secretome. Moreover, 188 secreted proteins were significantly and dynamically regulated during skeletal myogenesis, suggesting involvement in skeletal muscle development in either an autocrine or paracrine manner. Among the regulated secreted factors, we identified a family of proteins, the semaphorins, that are involved in muscle development. We investigated more closely the expression profiles of semaphorins at the mRNA and protein levels during the myoblast conversion. Finally, our approach resulted in the identification of 299 posttranslationally modified sites on 48 secreted proteins containing hydroxyproline residues and revealed a new motif distinct from the canonical hydroxyproline motif. To our knowledge, this also represents the largest data set of proline hydroxylation on secreted proteins. We thank all members from The Centre of Inflammation and Metabolism and the Center for Experimental BioInformatics for useful discussions. We are grateful to Dr. Carmen de Hoog (University of British Columbia, Vancouver, British Columbia, Canada), Dr. Jesper Olsen (The Novo Nordisk Foundation Center, Copenhagen, Denmark), and Dr. Joern Dengjel (Freiburg Institute for Advanced Studies, Freiburg, Germany) for critical reading of the manuscript. The Centre of Inflammation and Metabolism is supported by a grant from the Danish National Research Foundation. Supplementary Material Download .zip (4.56 MB) Help with zip files Download .zip (4.56 MB) Help with zip files

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.

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 candidatesaucune
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 enseignants0,016
Score d'incertitude au seuil0,650

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 technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,002
Tête enseignante GPT0,183
Écart entre enseignants0,181 · 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