Google Scholar Guide

def build_scholar_query(concepts: list[list[str]], exclude: list[str] = None,
                         title_only: bool = False, author: str = None,
                         source: str = None) -> str:
    """
    Build a structured Google Scholar query from concept groups.

    Args:
        concepts: List of concept groups, each a list of synonyms
                  Groups are ANDed together, synonyms are ORed
        exclude: Terms to exclude
        title_only: Search in title only
        author: Author name filter
        source: Journal/source filter
    Returns:
        Formatted Google Scholar query string
    """
    # Build concept groups with OR
    groups = []
    for concept_group in concepts:
        if len(concept_group) == 1:
            groups.append(f'"{concept_group[0]}"')
        else:
            terms = ' OR '.join(f'"{term}"' for term in concept_group)
            groups.append(f'({terms})')

    # AND the concept groups together
    query = ' '.join(groups)

    # Apply title restriction
    if title_only:
        query = f'intitle:{query}'

    # Add exclusions
    if exclude:
        for term in exclude:
            query += f' -{term}'

    # Add author filter
    if author:
        query += f' author:"{author}"'

    # Add source filter
    if source:
        query += f' source:"{source}"'

    return query

# Example: find papers on transfer learning for medical imaging
query = build_scholar_query(
    concepts=[
        ["transfer learning", "domain adaptation", "fine-tuning"],
        ["medical imaging", "radiology", "pathology images"],
        ["deep learning", "convolutional neural network"]
    ],
    exclude=["survey", "review"],
    title_only=False
)
print(query)
# Output: ("transfer learning" OR "domain adaptation" OR "fine-tuning")
#         ("medical imaging" OR "radiology" OR "pathology images")
#         ("deep learning" OR "convolutional neural network") -survey -review

Seed Paper (a highly relevant paper you already know)
  |
  +--> "Cited by" link -> Forward citation tracking
  |    (who cited this paper? newer related work)
  |
  +--> Reference list -> Backward citation tracking
       (what did this paper cite? foundational work)

Repeat for each highly relevant paper found.
Stop when you reach saturation (no new relevant papers appearing).

def identify_key_papers(search_results: list[dict],
                         min_citations: int = 10) -> list[dict]:
    """
    Identify key papers from search results using citation analysis.

    Args:
        search_results: List of papers with 'title', 'year', 'citations'
        min_citations: Minimum citation threshold
    """
    import datetime
    current_year = datetime.datetime.now().year

    for paper in search_results:
        age = max(1, current_year - paper['year'])
        paper['citations_per_year'] = paper['citations'] / age

        # Classify influence
        if paper['citations_per_year'] > 50:
            paper['influence'] = 'landmark'
        elif paper['citations_per_year'] > 20:
            paper['influence'] = 'highly_influential'
        elif paper['citations_per_year'] > 5:
            paper['influence'] = 'influential'
        else:
            paper['influence'] = 'standard'

    # Filter and sort
    filtered = [p for p in search_results if p['citations'] >= min_citations]
    return sorted(filtered, key=lambda x: x['citations_per_year'], reverse=True)