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Record W3112718257 · doi:10.18174/535987

Natural capital accounts for the North Sea : Suggestions for additions and valuation of ecosystem services

2020· report· en· W3112718257 on OpenAlexaff
Peter Roebeling, S.W.K. van den Burg, Maggie Skirtun, Katrine Soma, Katell G. Hamon

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsImpact
Fundersnot available
KeywordsNatural capitalValuation (finance)EcosystemEcosystem servicesSustainabilityEcosystem valuationNatural resourceNatural (archaeology)Environmental resource managementMarine ecosystemBusinessNatural resource economicsEnvironmental scienceEconomicsGeographyEcosystem healthAccountingEcology

Abstract

fetched live from OpenAlex

Natural capital accounts (NCA), also known as ecosystem accounts, is an approach for systematically measuring and monitoring the condition of ecosystems and corresponding ecosystem services over time, with the aim to support research, decision-making and planning. It is argued that natural resources will be more sustainably used and managed when the relationship between ecosystems and economic and other human activities is considered. In a reflection of the Statistics Netherlands (CBS) report Natural capital accounts for the North Sea: The physical SEEA EEA accounts (CBS, 2019), the objective of this report is to identify and improve ecosystems, conditions and physical accounts for the North Sea as well as to identify methods and sources to calculate values (prices) of ecosystem services provided, in preparation for the North Sea natural capital accounts.

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)0.009
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.011
Science and technology studies0.0010.001
Scholarly communication0.0060.010
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.008

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 head0.027
GPT teacher head0.253
Teacher spread0.227 · 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
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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