> ## Documentation Index
> Fetch the complete documentation index at: https://hubify.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Novelty Scoring

> AI-powered assessment of how novel your findings are, from incremental to field-changing, calibrated against existing literature.

# Novelty Scoring

Novelty Scoring is an AI-powered system that evaluates how novel a research finding is, calibrated against existing literature and known results. It helps you prioritize which results to pursue, which to publish, and which are incremental.

## How It Works

When an experiment produces a result, the novelty scorer:

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

| Score | Label              | Meaning                                                         |
| ----- | ------------------ | --------------------------------------------------------------- |
| 1-2   | **Incremental**    | Confirms known results. Marginal improvement over prior work.   |
| 3-4   | **Useful**         | New data point in a known area. Strengthens existing evidence.  |
| 5-6   | **Notable**        | Meaningfully extends the field. Worth a short paper or letter.  |
| 7-8   | **Significant**    | New constraint, prediction, or method. Worth a full paper.      |
| 9-10  | **Field-Changing** | Challenges established paradigms. Requires immediate follow-up. |

## Example Scores

| Finding                                                      | Score | Rationale                                                                         |
| ------------------------------------------------------------ | ----- | --------------------------------------------------------------------------------- |
| "MCMC confirms H0 = 67.68 in standard LCDM"                  | 2     | Matches known value. No new physics.                                              |
| "Matter bounce predicts f\_NL = -4.375, testable by SPHEREx" | 8     | Parameter-free prediction. Falsifiable by 2027. Novel across all bounce models.   |
| "Quintom-B favored at 2.3 sigma over LCDM"                   | 7     | Strong evidence for new physics, but not yet at discovery threshold.              |
| "ALP birefringence prediction matches 3.6 sigma observation" | 9     | Predicted value (0.27 deg) matches independent observation (0.342 +/- 0.094 deg). |

## Using Novelty Scores

Novelty scores feed into several workflows:

* **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

Novelty scoring uses cross-model evaluation to avoid inflated scores:

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

```bash theme={null}
# 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

```bash theme={null}
curl "https://www.hubify.com/api/v1/labs/your-lab-slug/experiments/EXP-054/novelty" \
  -H "Authorization: Bearer $HUBIFY_API_KEY"
```

```json theme={null}
{
  "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"]
}
```