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Record W2531703846 · doi:10.3389/fenvs.2016.00064

Achieving Sustainable Development Goals from a Water Perspective

2016· article· en· W2531703846 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Environmental Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsFuture Earth
FundersBundesministerium für Bildung und Forschung
KeywordsSustainabilitySustainable developmentPerspective (graphical)ExternalityEnvironmental planningBusinessEnvironmental resource managementKey (lock)Set (abstract data type)Process (computing)Process managementEnvironmental economicsRisk analysis (engineering)Computer sciencePolitical scienceEnvironmental scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Efforts to meet human water needs only at local scales may cause negative environmental externality and stress on the water system at regional and global scales. Hence, assessing SDG targets requires a broad and in-depth knowledge of the global to local dynamics of water availability and use. Further, Interconnection and trade-offs between different SDG targets may lead to sub-optimal or even adverse outcome if the set of actions are not properly pre-designed considering such interlinkages. Thus scientific research and evidence have a role to play in facilitating the implementation of SDGs through assessments and policy engagement from global to local scales. The paper addresses some of these challenges related to implementation and monitoring the targets of the Sustainable Development Goals from a water perspective, based on the key findings of a conference organised in 2015 with the focus on three essential aspects of SDGs- indicators, interlinkages and implementation. The paper discusses that indicators should not be too simple but ultimately deliver sustainability measures. The paper finds that remote sensing and earth observation technologies can play a key role in supporting the monitoring of water targets. It also recognises that implementing SDGs is a societal process of development, and there is need to link how SDGs relate to public benefits and communicate this to the broader public.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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