How a score is worked out › ledger-default@0.1.0

ledger-default@0.1.0

The everyday model. It scores every claim type that can be scored at all, including causal and interpretive claims, and marks those two as model-dependent so a reader knows another model may disagree.

Version 0.1.0. The algorithm as first specified: relevance, observation, interpretation, authenticity and extraction, strongest-only within each independence group, log-odds combination.

Released at entry seq 1 · config sha256:4cf1969d6a8c83c267b70414a149e38238b14566a8ebc8f11f65874a3086e12b · code sha256:b1357011dab1871f639065bec5574c8a4e4a3481ccbb754f52541f7a1a4d304c

A released model never changes. Those two hashes are recorded in every score it produced, so any number it has ever given can be reproduced exactly, by this node or by anyone else.

Where it differs from ledger-strict@0.1.0

Released alongside it, from the same code, with different settings.

Setting	ledger-strict@0.1.0	ledger-default@0.1.0
types it marks as model-dependent	none	causal, interpretive
observation weight: expert analysis	0.0	0.3
prior: causal	not declared	0.35
prior: interpretive	not declared	0.50
claim types it scores	observational, quantitative, historical, textual, comparative, definitional, attributed belief	observational, quantitative, historical, textual, comparative, definitional, attributed belief, causal, interpretive

What it scores, and what it declines to

Scored: observational, quantitative, historical, textual, comparative, definitional, attributed belief, causal, and interpretive.

Every other claim type gets not applicable from this model and no probability at all — a refusal to score, not a score of nought. It marks causal and interpretive as model-dependent, which says on the page that another model may reasonably disagree.

Every number it declares

What each of these means, and why it is there, is on how a score is worked out.

Prior by claim type

Setting	Value
causal	0.35
textual	0.50
historical	0.50
comparative	0.50
definitional	0.50
interpretive	0.50
quantitative	0.50
observational	0.50
attributed belief	0.50

Relevance

Setting	Value
weak	0.2
direct	2.0
strong	1.2
moderate	0.6
context only	0.0

Observation

Setting	Value
other	0.1
hearsay	0.1
eyewitness	0.4
calculation	0.7
direct text	0.9
measurement	1.0
model output	0.0
archaeological	0.9
dataset result	0.9
expert analysis	0.3

Authenticity

Setting	Value
doubtful	0.3
verified	1.0
unverified	1.0

Extraction

Setting	Value
verified	1.0
unverified	1.0

State thresholds

Setting	Value
supported	0.80
leans supported	0.60
unresolved above	0.40
leans contradicted above	0.20

The rest

Setting	Value
interpretive step penalty	0.15
independence	strongest only per group and direction
rounding	mode half_even, weights 6, coverage 2, probability 4, boundary guard 1e-6
review checks	primary source reviewed, opposing search done, independence reviewed, qualifiers reviewed
primary source types	primary text, dataset, measurement, legal document