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Record W2055152255 · doi:10.5539/jgg.v2n1p58

Detecting Vegetation Change in Neka River Basin of IRAN Based on

2010· article· en· W2055152255 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Geography and Geology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsWoodlandNormalized Difference Vegetation IndexLand useGeographyPhysical geographyLand coverDrainage basinVegetation (pathology)AgricultureLand degradationFragmentation (computing)Land use, land-use change and forestryEnvironmental scienceHydrology (agriculture)Climate changeGeologyEcologyCartographyArchaeology

Abstract

fetched live from OpenAlex

Land use change has transformed a vast part of the natural landscapes of the developing world for the last 50 years. Land is a fundamental factor of production and through much of the course of human history; it has been tightly coupled with economic growth. Bare soil has recently increased and it is one of the most important land degradation processes in the Mediterranean basins. The land use has changed rapidly within and near Neka River which is a fast growing agricultural river Basin. The land use changes in this region were analyzed based on Landsat data from 1977 to 2001. Supervised/unsupervised classi?cation approach coupled with GIS analyses was employed to generate the change over land use/cover maps. In order to analyze landscape fragmentation, land-use change has been calculated using NDVI. Based on the results of the analysis, the range of NDVI has changed from 0.9597/-0.2876 in 1977 to 0.6420/-0.187 in 2001 which shows that bare land has increased, while woodland areas decreased.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.216
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it