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Record W4404873153 · doi:10.1038/s44183-024-00095-1

Achieving at-scale seascape restoration by optimising cross-habitat facilitative processes

2024· article· en· W4404873153 on OpenAlexfundno aff
Maria L. Vozzo, Christina A. Buelow, Michael Sievers, María Fernanda Adame, Paul Branson, Joseph R. Crosswell, Christopher Doropoulos, Ben L. Gilby, Francisco Martínez‐Baena, Simon Reeves, Vera Rullens, Andy Steven, Ziyu Xiao, Kirk Dahle, Brian R. Silliman, R. ter Hofstede, Mark van Koningsveld, Megan I. Saunders

Bibliographic record

Venuenpj Ocean Sustainability · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaCommonwealth Scientific and Industrial Research Organisation
KeywordsSeascapeHabitatScale (ratio)Environmental scienceFisheryRestoration ecologyEcologyGeographyBiologyCartography

Abstract

fetched live from OpenAlex

Abstract Cross-habitat facilitative processes can enhance seascape restoration outcomes but there is uncertainty around the spatial dependencies of these processes across habitats. We synthesised the influence of environmental parameters on six processes underpinning cross-habitat facilitation and identified the linear distances over which they operate between habitats. All six process types occur at distances commonly used in seascape restoration demonstrating how harnessing facilitation can scale-up restoration to meet national and international goals.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.005
GPT teacher head0.254
Teacher spread0.250 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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