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Enregistrement W2143082294 · doi:10.1093/jxb/erp043

Known knowns, known unknowns, unknown unknowns and the propagation of scientific enquiry

2009· letter· en· W2143082294 sur OpenAlexaff
David C. Logan

Notice bibliographique

RevueJournal of Experimental Botany · 2009
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePhotosynthetic Processes and Mechanisms
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

In February 2002, Donald Rumsfeld, the then US Secretary of State for Defence, stated at a Defence Department briefing: ‘There are known knowns. There are things we know that we know. There are known unknowns. That is to say, there are things that we now know we don’t know. But there are also unknown unknowns. There are things we do not know we don’t know.’ As a result, he was almost universally lampooned since many people initially thought the statement was nonsense. However, careful examination of the statement reveals that it does make sense, indeed the concept of the unknown unknown existed long before Donald Rumsfeld gave it a new audience. Much scientific research is based on investigating known unknowns. In other words, scientists develop a hypothesis to be tested, and then in an ideal situation experiments are best designed to test the null hypothesis. At the outset the researcher does not know whether or not the results will support the null hypothesis. However, it is common for the researcher to believe that the result that will be obtained will be within a range of known possibilities. Occasionally, however, the result is completely unexpected—it was an unknown unknown. There are many known knowns of intracellular protein targeting and, as with many fields of research, it seems that the number of known unknowns increase in parallel. The key determinants for targeting to mitochondria have been determined, and there are at least four subclasses of mitochondrial-targeted proteins containing different targeting signals that are directed to different sites within the mitochondrion (outer membrane, inter-membrane space, inner membrane, and matrix) by different mechanisms (Bolender et al., 2008; Whelan and Glaser, 2007) (Fig. 1). However, much remains unknown, especially in plants. For example, there are now a number of known unknowns resulting from a previously unknown unknown: the existence of proteins dually targeted to both plastids and mitochondria (Peeters and Small, 2001; Ma and Taylor, 2002; Whelan and Glaser, 2007). We now know that dual targeting to plastids and mitochondria occurs as a result of ambiguous signal sequences, but we do not know how these signals are recognized by both organelles, when other proteins are only recognized by one (Whelan and Glaser, 2007). The paper by Chatre et al. (2009) in this issue is an excellent example of research uncovering unknown unknowns. Typically, investigations into the mechanics of intracellular protein targeting have been performed using protein biochemistry, but the investigation by Chatre et al. (2009) is not typical. If the study had simply been investigating mitochondrial targeting using in vitro translation of various proteins with altered targeting signals, detected by protein electrophoresis and immunoblotting, the results would have been a combination of ‘yes, the construct targets to mitochondria’ and ‘no, the construct does not target to mitochondria’. However, because a cell biological approach was taken, so much more information was garnered; that is the power of using fluorescent protein fusions in vivo. By means of a bioinformatics screen, Chatre et al. (2009) identified nucleus-encoded proteins that were predicted, based on the presence of a coiled-coil domain, to be targeted to the secretory pathway. However, one of the proteins identified, and named MITS1, was targeted to mitochondria when a full-length protein fusion was made to the N-terminus of YFP (Fig. 2A). MITS1, a putative actinbinding protein, was also correctly predicted, by various

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)0,001
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 enseignants0,321
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
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 Opus0,010
Tête enseignante GPT0,252
Écart entre enseignants0,242 · 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 ».

En bref

Citations108
Publié2009
Routes d'admission1
Résumé présentoui

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