Methodology
How SKALV knows what it knows.
SKALV separates incoming signals, observations, evidence, assessments and human verification so uncertainty is visible instead of compressed into a single score or alert.
A signal is not evidence
Evidence is a specific piece of information with a known origin and time. A signal is an interpretation that several pieces of evidence may support. Keeping the two apart means a signal can be re-examined later against what actually supported it.
Source provenance stays attached
Every piece of evidence keeps its origin: which connector or person delivered it, when it arrived, and in what form. Provenance travels with the evidence into signals, consequences and the archive, so nothing becomes anonymous as it moves.
Conflicting evidence is kept, not resolved away
When two sources disagree, SKALV does not average them into a middle value. The disagreement is retained and made visible, because a contradiction between sources is itself operationally meaningful.
Silence and staleness are information
A source that stops reporting is not the same as a source reporting that nothing is happening. SKALV tracks arrival, delay and silence separately from content, so the absence of information can be seen rather than assumed away.
Dependencies do not equal impact
Relating a signal to a node describes possible exposure. It does not assert that the node is affected. Confirmed impact requires supporting evidence or human verification, and remains distinguishable from possible exposure throughout.
Human verification is evidence
A verification task asks a specific person a specific question. The answer is recorded as evidence with its own origin and time, and it can strengthen an assessment, weaken it, or leave the uncertainty open. Verification is not a rubber stamp.
Foresight without false precision
Foresight describes possible developments together with the assumptions they rest on and the indicators that would support or undermine them. It does not produce probabilities the underlying evidence cannot justify.
See SKALV with your own operational scenario.
Bring a real scenario — the sources you follow, the functions you cannot lose — and we will show where SKALV fits and where it does not.