Bibliographic record
Abstract
In this article, I explore how the inherent cognitive limitations of human reasoning affect modelling practices in engineering research.By examining concepts such as emergence, uncertainty, and model usefulness, I argue that engineers must balance realism with practicality, often favouring effective approximations over theoretically "correct" but unusable models.Indeed, in complex systems, claims of realism might be particularly troublesome and misleading.The paper advocates for probabilistic reasoning, epistemic humility, and context-driven model selection as essential tools for advancing engineering knowledge.Theoretical models can still be perfectly useful when they accurately describe experiments, despite potential claims about the real world.[1] Novel techniques in bio-oil production through catalytic pyrolysis of waste biomass: Effective parameters, innovations, and techno-economic analysis Behnam RezvaniThis review article assesses novel techniques in bio-oil production through catalytic pyrolysis of waste biomass.The study analyzes critical parameters affecting bio-oil quality, including reaction temperature, heating rate, biomass feedstock type, and catalyst selection.The research explores innovations in catalyst design such as hierarchical zeolites, metal oxides, and bifunctional catalysts for improved deoxygenation and reduced coke formation.Advanced techniques like catalytic plasma pyrolysis and co-pyrolysis are investigated to enhance bio-oil characteristics.A comprehensive techno-economic analysis assesses the feasibility and scalability of these methods, contributing to the development of more efficient and economically viable renewable energy production from waste biomass.[2]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.359 | 0.169 |
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".