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Record W6948549881 · doi:10.5061/dryad.wpzgmsc0w

Pathways of blue carbon export from kelp and seagrass beds along the Atlantic coast of Nova Scotia

2025· dataset· en· W6948549881 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Image and Video Retrieval Techniques
Canadian institutionsDalhousie UniversityBedford Institute of Oceanography
Fundersnot available
KeywordsBlue carbonSeagrassKelp forestKelpCarbon sequestrationEcosystemCarbon fibersCarbon cycle

Abstract

fetched live from OpenAlex

Coastal vegetated ecosystems are recognized for their role in cycling and storing carbon in the world’s oceans (i.e., blue carbon); however, high uncertainty in carbon sequestration rates are driven in part by an absence of studies directly estimating carbon export to the deep sea. We modeled export from nearshore kelp forests and seagrass beds, showing that export varies by orders of magnitude across spatial scales (5 – 100s km), kelp and seagrass species, seasons, and forms of carbon, raising caution in using generalized export rates in blue carbon accounting. Our results are also the first to show rapid (within 20-30 days) and extensive export of neutrally buoyant dissolved organic carbon to the shelf break (up to 44% within 90 days), contrasting sinking particulate organic carbon particles that remained within 100 m water depth in the near shore. These results improve estimates of carbon sequestration by nearshore vegetated ecosystems and reveal contrasting patterns of export relative to other regions of the globe.

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)0.000
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.336
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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