diff --git a/src/thor/clusters.py b/src/thor/clusters.py
index 3ad64df3..ab0bdea8 100644
--- a/src/thor/clusters.py
+++ b/src/thor/clusters.py
@@ -49,7 +49,6 @@ def hash_obs_ids(obs_ids: List[str]) -> str:
 def drop_duplicate_clusters(
     clusters: "Clusters",
     cluster_members: "ClusterMembers",
-    num_cpus: int = 1,
 ) -> Tuple["Clusters", "ClusterMembers"]:
     """
     Drop clusters that have identical sets of observation IDs.
diff --git a/src/thor/orbit_determination/fitted_orbits.py b/src/thor/orbit_determination/fitted_orbits.py
index 37306733..b0050b9d 100644
--- a/src/thor/orbit_determination/fitted_orbits.py
+++ b/src/thor/orbit_determination/fitted_orbits.py
@@ -8,8 +8,6 @@
 from adam_core.coordinates.residuals import Residuals
 from adam_core.orbits import Orbits
 
-from ..utils.quivr import drop_duplicates
-
 __all__ = [
     "FittedOrbits",
     "FittedOrbitMembers",
@@ -124,7 +122,7 @@ def drop_duplicate_orbits(
             "coordinates.vz",
         ]
 
-    filtered = drop_duplicates(orbits, subset=subset, keep=keep)
+    filtered = orbits.drop_duplicates(subset=subset, keep=keep)
     filtered_orbit_members = orbit_members.apply_mask(pc.is_in(orbit_members.orbit_id, filtered.orbit_id))
     return filtered, filtered_orbit_members
 
diff --git a/src/thor/utils/__init__.py b/src/thor/utils/__init__.py
index 5ed6fb78..d1d288c0 100644
--- a/src/thor/utils/__init__.py
+++ b/src/thor/utils/__init__.py
@@ -2,4 +2,3 @@
 from .ades import *
 from .linkages import *
 from .logging import *
-from .quivr import *
diff --git a/src/thor/utils/quivr.py b/src/thor/utils/quivr.py
deleted file mode 100644
index a039281f..00000000
--- a/src/thor/utils/quivr.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from typing import List, Literal, Optional
-
-import numpy as np
-import pyarrow as pa
-import quivr as qv
-
-
-def drop_duplicates(
-    table: qv.AnyTable,
-    subset: Optional[List[str]] = None,
-    keep: Literal["first", "last"] = "first",
-) -> qv.AnyTable:
-    """
-    Drop duplicate rows from a `~quivr.Table`. This function is similar to
-    `~pandas.DataFrame.drop_duplicates` but it supports nested columns (representing
-    nested tables).
-
-    Parameters
-    ----------
-    table : `~quivr.Table`
-        Table to drop duplicate rows from.
-    subset : list of str, optional
-        Subset of columns to consider when dropping duplicates. If not specified then
-        all columns are used.
-    keep : {'first', 'last'}, default 'first'
-        If there are duplicate rows then keep the first or last row.
-
-    Returns
-    -------
-    table : `~quivr.Table`
-        Table with duplicate rows removed.
-    """
-    # Flatten the table so nested columns are dot-delimited at the top level
-    flattened_table = table.flattened_table()
-
-    # If subset is not specified then use all the columns
-    if subset is None:
-        subset = [c for c in flattened_table.column_names]
-
-    # Add an index column to the flattened table
-    flattened_table = flattened_table.add_column(0, "index", pa.array(np.arange(len(flattened_table))))
-
-    if keep not in ["first", "last"]:
-        raise ValueError(f"keep must be 'first' or 'last', got {keep}.")
-
-    agg_func = keep
-    indices = (
-        flattened_table.group_by(subset, use_threads=False)
-        .aggregate([("index", agg_func)])
-        .column(f"index_{agg_func}")
-    )
-
-    # Take the indices from the flattened table and use them to index into the original table
-    return table.take(indices)