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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 OpenAlex
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

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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