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Record W2342162015 · doi:10.5539/jsd.v9n3p1

Ecosystems and the Services They Provide

2016· article· en· W2342162015 on OpenAlexvenueno aff
Molla Mekonnen Alemu

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesBusinessEcosystemBiodiversityEnvironmental resource managementAction (physics)Face (sociological concept)Natural resource economicsEnvironmental planningEcologyEnvironmental scienceEconomicsBiology

Abstract

fetched live from OpenAlex

Every life form is dependent on ecosystem services for food, water, fiber, ecological regulation and societal values. During the past 50 years, human beings interfered in to the wellbeing of these ecosystems and the services they provide, mainly to fulfill the ever increasing demand of humans. Many ecosystems are being degraded by putting their ecological services in jeopardy and causing considerably irremediable losses of biodiversity from the face of the earth. The degradation of these resources is also expected to grow in the years to come by posing a threat to the wellbeing of humanity in various forms. The threat, therefore, needs a collaborated action from grass root to global level entities and bodies. Strong policy, strategy and institutional frameworks and action plans could also play a role in averting the ongoing degradation of the ecosystem services. This paper is therefore, aimed at highlighting the major ecosystem services and some possible measures that will help to mitigate the degradation of their services.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.006

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 head0.005
GPT teacher head0.166
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations2
Published2016
Admission routes1
Has abstractyes

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