← all works
Safety Stock Estimation Based on Forecasted Demand Distribution Using Recurrent Mixture Density Networks
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
The three-model screen
all 1,000 screened works →All three models called this out of scope.
stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high
Operations research on safety stock estimation using recurrent mixture density networks; a supply chain forecasting method for inventory, not a study of research.
GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high
This work develops a demand-forecasting approach for safety-stock estimation, not a study of research.
Grok 4.5OUT
genre: empirical
about Canada: no
confidence: low
Operations/forecasting title on safety stock estimation; domain application, abstract missing.
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- SSRN Electronic Journal
- Topic
- Forecasting Techniques and Applications
- Field
- Decision Sciences
- Canadian institutions
- Concordia University
- Funders
- —
- Keywords
- EstimationDensity estimationEconometricsStock (firearms)Distribution (mathematics)StatisticsEconomicsMathematicsGeography
- Has abstract in OpenAlex
- no