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Record W3097572033 · doi:10.1007/s10811-020-02256-4

Saved by seaweeds: phyconomic contributions in times of crises

2020· article· en· W3097572033 on OpenAlexaff
Ole G. Mouritsen, Prannie Rhatigan, M. Lynn Cornish, Alan T. Critchley, José Lucas Pérez‐Lloréns

Bibliographic record

VenueJournal of Applied Phycology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsCape Breton UniversityAcadian Seaplants (Canada)
FundersNordea-fonden
KeywordsPlant physiologyBiologyBotany

Abstract

fetched live from OpenAlex

Seaweeds (macroalgae) are, together with microalgae, main contributors to the Earth's production of organic matter and atmospheric oxygen as well as fixation of carbon dioxide. In addition, they contain a bounty of fibres and minerals, as well as macro- and micronutrients that can serve both technical and medicinal purposes, as well as be a healthy and nutritious food for humans and animals. It is therefore natural that seaweeds and humans have had a myriad of interwoven relationships both on evolutionary timescales as well as in recent millennia and centuries all the way into the Anthropocene. It is no wonder that seaweeds have also entered and served as a saviour for humankind around the globe in many periods of severe needs and crises. Indeed, they have sometimes been the last resort, be it during times of famine, warfare, outbreak of diseases, nuclear accidents, or as components of securing the fabric of social stability. The present topical review presents testimony from the history of human interaction with seaweeds to the way humankind has, over and over again, been 'saved by seaweeds'. It remains a historical fact that in extreme conditions, such as shortage and wars, humans have turned to seaweeds in times of 'needs must' and created new opportunities for their uses in order to mitigate disasters. Lessons to be learned from this history can be used as reminders and inspiration, and as a guide as how to turn to seaweeds in current and inevitable, future times of crises, not least for the present needs of how to deal with changing climates and the pressing challenges of sustainable and healthy eating.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.000

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 head0.007
GPT teacher head0.212
Teacher spread0.206 · 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.

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

Citations52
Published2020
Admission routes1
Has abstractyes

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