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Record W2233237364

Blue Carbon in the Comox Valley: monetizing the benefits of eelgrass habitat restoration in coastal British Columbia

2014· article· en· W2233237364 on OpenAlexaboutno aff
C.A. Hodgson

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatRestoration ecologyEnvironmental scienceEcologyGeographyFisheryEnvironmental protectionBiology
DOInot available

Abstract

fetched live from OpenAlex

The restoration of eelgrass and salt marsh beds in areas where they formerly existed is widely recognized as a valuable activity due to their importance as habitat for estuarine inhabitants and for foreshore resilience. These restoration activities can also play a role in sequestering carbon dioxide from the atmosphere and putting it into long-term storage. Blue Carbon, where aquatic plants act to store carbon in the sediments and biomass, is another benefit to eelgrass and salt marsh rehabilitation. The Blue Carbon Team, consisting of professional and volunteer members located in the Comox Valley on Vancouver Island, British Columbia, is pursuing the opportunity to develop a protocol for measuring the amount of carbon permanently sequestered by eelgrass and salt marsh, while at the same time restoring habitat that had been lost during the past 75 years due to urbanization of the area. Preliminary data suggests estuarine plant communities can remove carbon dioxide from the atmosphere and store it in sediments and biomass more efficiently than land plants. Thus, these habitat restoration efforts would additionally contribute towards mitigating climate change and could be used as carbon credits to monetize further habitat restoration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.703

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 science0.0010.000
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 head0.013
GPT teacher head0.179
Teacher spread0.166 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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