Evolution of the forest cover in Cote d'Ivoire since 1960 to the beginning of the 21st century
Bibliographic record
Abstract
Deforestation is a phenomenon that is a reality in Cote d'Ivoire. Thus, this study aims to determine the spatio- temporal evolution of her moist forests. For the 1960s, the moist forest cover of the country is determined through the vegetation mapping by digitizing. Those of decades 1980 and 2000 are obtained by treatment of Landsat TM and ETM+ satellite images. Supervised classification by maximum likelihood allowed getting maps of the land with, cartographic accuracy greater than 90%. Of these maps, the class of moist forests was extracted and converted to vector data format. The intersection of moist forests of decades 1980 and 2000 with those of the 1960s was used to determine their specific characterization. The results show a moist forest cover of about 8.14 million ha for 1960s with the presence of large blocks. The 1980s and 2000s have respectively forest cover of about 2.6 million ha and 1.35 million ha. These periods are characterized by high fragmentation and loss of rain forests in Cote d'Ivoire. Thus, since its independence, the country has lost more than 80% of its forest cover. Agricultural clearings related to demographic pressure and logging are the main causes of deforestation in Cote d'Ivoire.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".