On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rules

Manuscript statistics

Major revisionpanel verdict · 2026-09-01

Manuscript Statistics — On the sensitivity of machine-learned probabilistic weather forecast

Measured deterministically at ingest, with no model involved. Every figure describes the converted text the panel read, not the PDF.

How the file converted

  • Format: markdown via rustypaper 0.2.0
  • Section map: read from the document model
  • Conversion health: clean
  • Fused tokens: 0.0 per 1000 words
  • Hyphenated line breaks: 0.0 per 1000 words
  • Lost sentence spaces: 0.0 per 1000 words
  • Markdown headings emitted by the converter: 23
  • Blank-line-separated blocks: 144
  • Text matching no known section: 0%

Size

  • Words: 5,160
  • Main text (excluding references): 4,381
  • Reference list: 779 words
  • Sentences: 238
  • Display equations: 48
  • Table rows: 21

Prose

  • Sentence length: mean 21.68, median 20.0, 90th percentile 35.0 words
  • Sentences over 40 words: 6%
  • Lexical diversity (MATTR): 0.457
  • Passive constructions: 0.2773 per sentence (regex approximation)

Claims and evidence

  • Citations: 64 (12.4 per 1000 words), style numeric
  • Bibliography: 25 entries typed by the converter
  • Numbers: 52.33 per 1000 words
  • Hedging language: 3.68 per 1000 words
  • Amplifying language: 0.39 per 1000 words
  • p-values: 0 exact, 0 reported only as a threshold

By section

Measured over each section separately. The bibliography is left out: hedging and sentence length over a reference list describe a dozen journals' house styles rather than this manuscript.

SectionWordsSentencesMean sentenceCitations/1kHedges/1kBoosters/1k
_preamble20120.00.00.00.0
abstract1901019.00.015.790.0
introduction2,88412423.2611.11.390.35
results7524616.351.337.981.33
discussion4312021.5513.9213.920.0
acknowledgements96332.00.00.00.0

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