1 The Importance Of Time Dependence In Functional Modeling A Case Study Comparing Modeling Methods for an Agricultural Process Application
Bibliographic record
Abstract
Several methods of modeling systems are common in TRIZ for problem solving, system simplification, and improvement. Subject – Action – Object models are based on specific physical components of a system and the explicit effects they exert on other components of the system, and are derived from the Su-field modeling method of classical TRIZ. “Operational ” models, generally known as “problem formulator ” models, allow broader definition of a system’s elements to include actions as well as components. Both models focus on whether those elements ultimately contribute useful or harmful effects to the primary purpose of the system or to elements supporting that purpose. There is no direct way of using these models to deal with time-variable elements in the system model, or to deal with different aspects of a problem that occur at different levels of the system. Most TRIZ teachers emphasize the need to clearly identify the problems at each level of the system, and to solve them separately. Causal loop models are useful in TRIZ to identify the system elements and actions in a system that are most pertinent to causing a desired change in performance, and add a powerful ability to understand the time dependence of conflicts. Organizing the views of the problem in a hierarchy of macro-performance, trends and patterns, accumulation of discrete results, and discrete events also clarifies the situation and makes the solution space more accessible. A Canadian agricultural problem will be used to illustrate these issues. Flax straw from plants grown to produce seeds for oil is extremely tough material. Deciding whether to burn the straw, sell it for use as fiber or in wood-substitute products, or use it to feed cattle requires that farmers make many time-dependent and condition-dependent decisions to survive in the highly variable economic and physical environment in which they function. 2 1.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".