yogsoth-ai Parameter Screening
Quickly identify important vs unimportant parameters.
Budget
| Base SOP | Target | ±10% Range |
|---|---|---|
| web-search | 20 | 18–22 |
| web-research | 5 | 4–6 |
| paper-overview | 30 | 27–33 |
| paper-search | 20 | 18–22 |
| paper-research | 10 | 9–11 |
State Ledger
<HARD-GATE>
| SOP | Done | Target | % |
|-----|------|--------|---|
| web-search | ? | 20 | ? |
| web-research | ? | 5 | ? |
| paper-overview | ? | 30 | ? |
| paper-search | ? | 20 | ? |
| paper-research | ? | 10 | ? |
Budget Gate: OPEN/CLOSED (>=80% required to exit)
</HARD-GATE>
Available Tactics
- screening-then-decomposition
Available SOPs
Import: web-search, web-research, paper-overview, paper-search, paper-research Subagent: morris-screening
Execution Guidance
Use Morris method (one-at-a-time with random trajectories) to quickly identify which parameters have large effects (high mu*) and which have nonlinear/interaction effects (high sigma). Eliminate clearly unimportant parameters.
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| screening-then-decomposition | Two-phase sensitivity — Morris quick screening to eliminate unimportant factors, then Sobol precise decomposition on survivors. Efficient allocation of analytical effort. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| morris-screening | Morris method screening — compute elementary effects to quickly identify important vs unimportant parameters. |