Whatsoever things are true: Hypothesis, artefact, and bias in chemical engineering research
Bibliographic record
Abstract
Abstract For experimental research to offer valuable insights and predictions, its results and interpretation need to be properly validated. When the experiments deal with complex systems, such as biological materials, multicomponent mixtures, or multiple phases, the use of the rigorous scientific method is essential. Setting and testing hypotheses and design of experimental programs to include formal positive and negative experiments helps to identify artefacts and to minimize the influence of the biases of the investigators that can invalidate the results of studies. The literature has many examples of well‐executed studies, but there is much less discussion of the pitfalls and traps that can beset experimental research. This paper presents cases in the area of chemical reaction engineering and biochemical engineering. Experimental designs are presented that were successful in validating results by using positive and negative controls. Case studies of experimental artefacts that were published suggest that the biases of the investigators were important in failing to fully verify experimental observations. Both strong experimental designs to identify artefacts and publication of negative results are important in avoiding the persistence of what Stephen Poole calls “zombie ideas”. These zombie ideas may be benign, or they may lead to considerable wasted effort on studies with no chance of success. Graphical representations and schematics are extremely valuable in communicating results and making them memorable, but they can also be seductive in misleading researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.534 | 0.701 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.081 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".