Known knowns, known unknowns, unknown unknowns and the propagation of scientific enquiry
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".