Explicit vs Generic Data
Nutrient data in ingredient databases comes in two forms: explicit values (specifically measured and reported) and generic keys (broad categories that may include multiple nutrient subtypes). RawPawIQ preserves this distinction rather than estimating missing values, which means some nutrients may show as zero when no explicit data exists.
How It Works
- 1
Explicit Values Are Stored Directly
When USDA or manufacturer data includes a specific nutrient measurement, that exact value and unit are stored. For example, "Vitamin D3 (cholecalciferol): 5 mcg" is an explicit value.
- 2
Generic Keys Represent Categories
Some USDA entries use generic keys like "Vitamin D" without specifying D2 or D3. These generic values cannot be split into subtypes without guessing.
- 3
Missing Data Shows as Zero
If a nutrient wasn't measured or reported in the source data, RawPawIQ shows zero rather than estimating a value. Zero means "not measured," not "not present."
- 4
No Fallback to Generic Totals
The system does not substitute generic totals when specific values are needed. This prevents overstating nutrient content with potentially incorrect data.
Examples
| Data Type | Example Key | What It Means |
|---|---|---|
| Explicit | pufa_20_5_n_3_epa | EPA specifically measured and reported |
| Explicit | vitamin_d3_cholecalciferol | D3 form specifically identified |
| Generic | pufa_18_3 | Could be ALA (n-3) or GLA (n-6) - ambiguous |
| Combined | vitamin_d_d2_plus_d3 | D2/D3 form not specified on label |
Why This Exists
The principle is simple: showing nothing is better than showing wrong data. Generic keys like "PUFA 18:3" could represent either omega-3 (ALA) or omega-6 (GLA) fatty acids - the chemical notation doesn't specify. Rather than guess which one it is, the system shows zero for the specific nutrient until explicit data is available. This prevents false confidence in potentially incorrect nutrient totals.
Common Misinterpretations
"Zero means the food has none of that nutrient"
Zero typically means "not measured" rather than "not present." Salmon contains omega-6, but if only generic PUFA keys exist, the explicit omega-6 value shows zero.
"The system should estimate missing values"
Estimation introduces error. A key like pufa_18_3 could be 100% ALA or 100% GLA or any mix. Guessing would corrupt the data integrity the system is built on.
"Explicit data is always complete"
Even Foundation Foods may not have all nutrients measured. Amino acids, specific fatty acids, and some vitamins are frequently missing from older datasets.