Analysis of Land Use and Land Cover Changes in the Wetland Ecosystem of Port-Harcourt Metropolis, Nigeria
Bibliographic record
Abstract
The study analyzed changes in land use and land cover overtime in Port-Harcourt metropolis using remote sensing techniques from 1984-2013.The Geo-referencing properties of 1984, 1999, 2003 and 2013 include; universal Transverse Mercator (UTM) projection, and datum WGS 84, zone 32, ERDAS Imagine (2014).These were used for displaying processing, enhancement, classification of the imageries and also used for the delineation of the study area imagery. IDRIS SELVA was used for the development of land use land cover classes. ArcGIS 10.1was used in developing, display and processing of the location maps. Total percentage change and total rate of change for the entire trend after conversion are thus: Saltwater Wetland 23.44%, to- 2.17, Freshwater Wetland - 26.44%to- 11.49 Fallow land- 47.13%to- 5.41 Built-up Area 43.33% to 7.41Water bodies 43.36% to 3.06.The study recommended that activities shrinking wetlands size should be thoroughly put to check by the government and better efforts should be focused on those activities that inspire wetland conservation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".