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Record W4405642934 · doi:10.1590/scielopreprints.10876

Beyond scents: fragrance industry partnerships for biodiversity conservation

2024· preprint· en· W4405642934 on OpenAlexaboutno aff
Luiza F. A. de Paula, Rhian J. Smith, Vanessa Handley, Alexandre Antonelli, Peggy L. Fiedler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and biochemical processes
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisVetenskapsrådetCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorStiftelsen för Miljöstrategisk Forskning
KeywordsBiodiversityBusinessBiodiversity conservationEnvironmental resource managementEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Since the 1992 Convention on Biological Diversity at the Earth Summit in Rio de Janeiro, United Nations biodiversity agreements have evolved to provide greater specificity on benefit-sharing and conservation strategies. The Kunming-Montreal Global Biodiversity Framework additionally highlights new opportunities for businesses to support biodiversity conservation, reinforced by the creation of a landmark ‘Cali Fund’ at COP16 in Colombia. However, biodiversity continues to decline at an alarming rate. This paper explores how innovative partnerships between the fragrance industry, conservation NGOs (both global and local), and commercial brands can advance international plant conservation goals. By using the scents of threatened species and ecosystems as inspiration for novel products without destructive harvesting, companies can integrate conservation principles into product development, from sourcing to commercialization, while channeling a portion of proceeds back into conservation efforts. This approach directly connects industries to conservation and addresses a critical gap in plant science expertise within leadership roles in both conservation organizations and businesses. Greater engagement by conservation scientists with the fragrance industry is needed, offering a replicable model for corporate engagement that is sustainable, equitable, and impactful. To achieve these goals, bioprospecting must be reframed to address current environmental, market, and societal challenges.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0020.001
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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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 designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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