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Ecological economics of soil erosion: a review of the current state of knowledge

2011· review· en· W1545596551 on OpenAlexaff
Bhim Adhikari, Karthik Nadella

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2011
Typereview
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsMcMaster UniversityUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsValuation (finance)Land degradationStrengths and weaknessesEcosystem servicesLand useLand managementEnvironmental resource managementNatural resource economicsEcological economicsEnvironmental scienceEcosystemEconomicsEcologySustainability

Abstract

fetched live from OpenAlex

The economics of land degradation has received relatively little attention until recent years. Although a number of studies have undertaken valuation of ecosystem services ranging from the global to the micro level, and quite a few studies have attempted to quantify the costs of soil erosion, studies that address the full costs of land degradation are still scarce. In this review, we attempt to analyze different land resource modeling and valuation techniques applied in earlier research and the type of data used in these analyses, and to assess their utility for different forms of land resource and management appraisal. We also report on the strengths and weaknesses of different valuation techniques used in studies on the economics of soil erosion, and the relevance of these valuation techniques. We make a case for the need for more appropriate models that can make the analysis more robust in estimating the economic costs of land degradation while recognizing the spatial heterogeneity in biophysical and economic conditions.

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 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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.163
GPT teacher head0.355
Teacher spread0.193 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2011
Admission routes1
Has abstractyes

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