Analysis of Ecological Balance Issue for the Built-Up Land and Cropland Footprints in Alexandria City, Egypt During this Time-Series (2005-2019)
Bibliographic record
Abstract
At recent times, rapid urbanization growth occurs in numerous cities, thus this creates many issues, leading to local ecological degradation.So, an evaluation tool has been proposed to measure this ecological balance issue (EBI) to assess the urban sustainability of a city which is an Ecological Footprint Analysis (EFA) tool.This paper aims to measure the imbalance of consumption/production of built-up land in Alexandria city by using the EFA tool.In order to assess a holistic picture of the urban sustainability of built-up land, the researcher collected all the relevant data during this time-series (2005-2019) from the local authorities.In the accounts of ecological footprint (EF), the parameters of built-up land are set as equal to those of cropland, based on the assumption that built-up land is totally converted from cropland.However, built-up land may be derived from other types of land use, but the cropland ranks as the most productive use.So, one of the objectives of this paper is to compare between the builtup land and cropland to ascertain the extent of loss on cropland.The researcher concludes that the Ecological Footprint (EF) of built-up land is larger than the bio-capacity (BC) of built-up land, resulting in an existing ecological balance issue (ecological deficit), this can be considered as urban unsustainable pattern.Consequently, the researcher has been suggested guidelines and recommendations responding to the final results of measurements so that more decisions can be taken to move towards the urban sustainability progress by observing the local realities for Alexandria's vision of 2050.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".