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Record W1965649199 · doi:10.2495/sdp-v1-n4-419-428

Ecological evaluation of land

2006· article· en· W1965649199 on OpenAlexvenueno aff
Davide Geneletti

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsnot available
FundersUniversiteit van Amsterdam
KeywordsStructuringEnvironmental resource managementLand useEcologyComputer scienceEcological indicatorManagement scienceEnvironmental planningEnvironmental scienceBusinessEngineeringEcosystem

Abstract

fetched live from OpenAlex

The ecological evaluation of land aims at providing information that can be used to support decision making in nature conservation.It can be seen as the link between the science of ecology and the practice of land management.This paper presents a review of the approaches proposed in the literature to perform ecological evaluation, focusing in particular on the different objectives and criteria, as well as on the way in which they are assessed and on the use of relevant indicators.In addition, the paper proposes a survey on the shortcomings found in current applications.The main problems affecting the practice of ecological evaluation concern the structuring of the evaluation framework: objectives are often poorly defined and not clearly linked to the relevant criteria.In turn, the criteria show problems of correlation and are seldom assessed by means of explicit indicators.This clouds the overall results of the evaluation and undermines their practical use in planning-related procedures.

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 designNot applicable
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

Citations3
Published2006
Admission routes1
Has abstractyes

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