Analyzing STRUCTURE OF ALZAHRAA QUARTER MARKET IN MOSUL CITY STUDY IN URBAN GEOGRAPHY
Bibliographic record
Abstract
The area of traditional markets is not considered the only focus of the only marketing which received the population of Mosul and its region . but the functional and structural exchanges have faced the city especially after the expansion of its size and area . but many commercial focuses which appear in Mosul city especially the commercial market of al Zahra quarter after 1986. The variation in the pattern and the distribution of the marketing center in al Zahra quarter requires the city geography researchers to do distinct work through analyzing such a geography to focus on determining its locations by studying its geographicall distribution and to try determining the levels of its importance which services the goals of urban planning when trying to plan for it inside urban space . The commercial market of al Zahra contains 230 functional units and patterns the shops of sale and purchase are dominant over other patterns that about 172 commercial shops with 47,8 % the percentage from the total of shops is then the service shops which reach to 36 commercial shops and their percentage from the total shops ,Finally the other patterns from only just 9,5% from the total of the commercial shops in the markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".