Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2253485631
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. Frikha (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Training Effectiveness: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| A Tunisian manufacturer reports 2.25 million TND in annual cost savings and lower emissions after replacing traditional forecasting with CNN‑LSTM models, which improved demand-forecast accuracy and operational efficiency; the finding comes from a single-firm before‑and‑after analysis and may not generalize where data fragmentation and skills shortages persist.openalex | M. Frikha provider id |
2026-01-01 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.