Command line

# subsets and reference statistics
geclens subset  --ref ref.m2 --type R:VERB:SVA --out subsets.json

# scores with 95% bootstrap confidence intervals
geclens score   --src src.txt --ref ref.m2 --type DET \
                --hyp sysA=a.txt sysB=b.txt --ci 2000 --out-dir results

# paired approximate randomisation, Holm-adjusted, every system vs a baseline
geclens compare --src src.txt --ref ref.m2 --type PREP --hyp sysA=a.txt sysB=b.txt base=c.txt \
                --baseline base --measures type_only overcorrection --shuffles 10000

# system x error-type heatmap of ERRANT F0.5
geclens profile --src src.txt --ref ref.m2 --hyp a.txt b.txt c.txt --group category --top 12

# everything from one configuration file
geclens run config.yaml

Inputs: - --src: tokenised source sentences, one per line. - --ref: an ERRANT-typed reference M2 file. If you only have corrected text, create it with errant_parallel -orig src.txt -cor ref1.txt [ref2.txt ...] -out ref.m2. - --hyp: system outputs as text (one sentence per line) or as ready-made .m2 files.

Hypothesis M2 files are built once with ERRANT and cached.

Output files

File	Content
scores.csv / .md / .tex	One row per system; the Markdown and LaTeX tables include 95% CIs
<system>.json	All measures and per-type ERRANT counts
confidence_intervals.json	Bootstrap intervals
comparisons.md / .json	Differences, paired-bootstrap CIs, p-values and Holm-adjusted p-values
scatter.png	Type-only F0.5 against over-correction
profile.png / .json	System × error-type heatmap
subsets.json, reference_stats.json	Subset indices and reference statistics