​

Novelty Scoring

​

How It Works

1. Extracts the key finding, What is the scientific claim?
2. Searches existing literature, Has this been reported before? How does it compare?
3. Evaluates significance, Statistical strength, theoretical implications, testability
4. Cross-references the knowledge base, Does this connect to other findings in the lab?
5. Assigns a score, 1 to 10 scale with a written justification

​

Scoring Scale

​

Example Scores

​

Using Novelty Scores

• Experiment prioritization, Higher-novelty follow-ups get queued first
• Paper readiness, A paper’s overall novelty influences publication priority
• Lab site highlights, High-novelty results are featured prominently on the public site
• Resource allocation, GPU time is prioritized toward high-novelty research directions

​

Cross-Model Calibration

1. The primary model scores the finding
2. A second model from a different provider reviews the score
3. If scores diverge by more than 2 points, a third model breaks the tie
4. The final score is the median of all evaluations

​

CLI

# Score a specific experiment's results
hubify experiment score EXP-054

# View novelty scores for all experiments
hubify experiment list --sort novelty

# Get detailed novelty report
hubify experiment score EXP-054 --verbose

​

API

curl "https://www.hubify.com/api/v1/labs/your-lab-slug/experiments/EXP-054/novelty" \
  -H "Authorization: Bearer $HUBIFY_API_KEY"

{
  "experiment_id": "EXP-054",
  "score": 8,
  "label": "Significant",
  "finding": "Matter bounce predicts f_NL = -4.375, parameter-free and testable by SPHEREx",
  "rationale": "Parameter-free prediction distinguishes bounce from inflation. SPHEREx forecast shows 4.7-12 sigma detection by 2027. No prior work has derived this specific value.",
  "literature_matches": 3,
  "reviewed_by": ["claude-opus-4-8", "gpt-5.5", "gemini-3.1-pro", "grok-4", "sonar-pro"]
}