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Record W7036156521

Blue Carbon Opportunities for the Lower Shoalhaven River

2023· article· en· W7036156521 on OpenAlexfundno aff

Bibliographic record

VenueResearch Online (University of Wollongong) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
FundersMcMaster University
KeywordsBlue carbonTributaryFloodplainEcosystemClimate changeHydrology (agriculture)Carbon fibersLand useCarbon cycle
DOInot available

Abstract

fetched live from OpenAlex

Blue Carbon Ecosystems (BCE) play an important role as profound carbon reservoirs and have the potential to sequester more carbon in an area than any other ecosystem. This has sparked interest in these ecosystems and their potential for use in climate mitigation strategies and more specifically, carbon abatement. BCEs can be restored in areas which have been tidally modified through anthropogenic interventions. One such location which has experienced anthropogenic tidal regime modification is the Lower Shoalhaven River region - a region that has a broad floodplain with many floodgates separating rivers, creeks, and associated tributaries from the tidal influences of the Shoalhaven River Estuary. Recent studies have investigated the Blue Carbon potential at a national and state level, but there are limited studies on the application on a regional scale. Accordingly, using the Blue Carbon Accounting Model (BlueCAM) and its derivative, the Forward Abatement Estimator (FAE) this study aimed to 1. Identify Blue Carbon abatement potential across the Lower Shoalhaven Floodplain, 2. Investigate abatement upstream of a floodgate as a case study to provide insight into tidal restoration of Blue Carbon Ecosystems. 3. Identify social, economic, and environmental impacts of tidal restoration and recommend strategies to overcome these impacts and maximise abatement. Abatement potential was determined using LiDAR data in conjunction with land use mapping, to create elevation layers separated by land use and stratified to enable FAE and BlueCAM abatement values. This was done for a 25 year and 100 year permanence periods. Abatement potential and suitability for BCE ecosystems was also interrogated upstream of a single floodgate and if this floodgate had a limited opening. This study found that areas with low carbon stocks were more suitable for Blue Carbon projects due to these areas having lower ecosystem transition costs. Areas with lower elevations were deemed less suitable for Blue Carbon projects as these areas would go through multiple ecosystem transitions imposed by the variability in salinity levels by anthropogenic climate change and more specifically, sea level rise. The Lower Shoalhaven River was estimated to have the potential for high abatement, worth millions of dollars. It was found that the FAE was better suited to determining future abatement potential when compared to BlueCAM, but the latter was more suited to reporting on ongoing Blue Carbon project. This study recommends the investigation and potential modification of the Forward Abatement Estimator by the ERF, with the possibility of inclusion of the FAE in the tidal restoration of Blue Carbon ecosystems method. Further study into sites identified in this study as having a high abatement potential should be conducted with greater resolution. Finally, studies should be conducted to identify values, concerns and issues of landholders and communities since such engagement is necessary to ensure a balance between the use of the land and the pursuit of carbon abatement opportunities. Overall, this study recommends that Blue Carbon projects in the Lower Shoalhaven region should target grazing areas and work alongside affected landholders to achieve triple bottom line outcomes – economic, environmental and social sustainability.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.330

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.149
GPT teacher head0.320
Teacher spread0.171 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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