diff --git a/scientific-skills/tiledbvcf/references/export.md b/scientific-skills/tiledbvcf/references/export.md
index db3b8cf..a52f641 100644
--- a/scientific-skills/tiledbvcf/references/export.md
+++ b/scientific-skills/tiledbvcf/references/export.md
@@ -62,72 +62,6 @@ ds.export_tsv(
 )
 ```
 
-### Custom Field Selection
-```python
-# Define custom TSV fields
-custom_fields = [
-    "CHR",           # Chromosome
-    "POS",           # Position
-    "ID",            # Variant ID
-    "REF",           # Reference allele
-    "ALT",           # Alternative allele
-    "QUAL",          # Quality score
-    "FILTER",        # Filter status
-    "I:AF",          # INFO: Allele frequency
-    "I:AC",          # INFO: Allele count
-    "I:AN",          # INFO: Allele number
-    "S:GT",          # Sample: Genotype
-    "S:DP",          # Sample: Depth
-    "S:GQ"           # Sample: Genotype quality
-]
-
-ds.export_tsv(
-    uri="detailed_variants.tsv",
-    regions=["chr1:1000000-2000000"],
-    samples=["SAMPLE_001", "SAMPLE_002"],
-    tsv_fields=custom_fields
-)
-```
-
-### Population-Specific Exports
-```python
-def export_population_data(ds, regions, population_file, output_prefix):
-    """Export data for different populations separately"""
-    import pandas as pd
-
-    # Read population assignments
-    pop_df = pd.read_csv(population_file)
-
-    populations = {}
-    for _, row in pop_df.iterrows():
-        pop = row['population']
-        if pop not in populations:
-            populations[pop] = []
-        populations[pop].append(row['sample_id'])
-
-    # Export each population
-    for pop_name, samples in populations.items():
-        output_file = f"{output_prefix}_{pop_name}.tsv"
-
-        print(f"Exporting {pop_name}: {len(samples)} samples")
-
-        ds.export_tsv(
-            uri=output_file,
-            regions=regions,
-            samples=samples,
-            tsv_fields=["CHR", "POS", "REF", "ALT", "S:GT", "S:AF"]
-        )
-
-        print(f"Exported {pop_name} data to {output_file}")
-
-# Usage
-export_population_data(
-    ds,
-    regions=["chr1:1000000-2000000"],
-    population_file="populations.csv",
-    output_prefix="population_variants"
-)
-```
 
 ## Pandas DataFrame Export