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Dynamics of dispersal: Modeling the impact of dandelion proliferation on environmental and economic equilibria

2024· article· en· W4399984770 on OpenAlex
Leyi Jiang, Mingjun Ouyang, Weiyu Qi, Yueyue Yu

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTheoretical and Natural Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDandelionBiological dispersalCreaturesSeed dispersalPopulationDifferential (mechanical device)Fuzzy logicSensitivity (control systems)GeographyEcologyComputer scienceBiologyEngineeringArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

Dandelion, with its unique wind dispersal mechanism, can easily spread its seeds across vast land areas, encroaching on territories of other creatures. To address this issue, two models are considered: The first model considers wind patterns, temperature, and moisture in dandelion growth, using differential equations to analyze population changes over time. Sensitivity analysis reveals the model’s responsiveness to these factors. The second model combines ecological and economic factors to assess the impact of invasive species, employing fuzzy mathematics to determine the severity of impact. The study identifies Canada Goldenrod as particularly destructive. These models offer insights for future invasive species management policies, showcasing innovations in incorporating partial differential functions and fuzzy mathematics for more accurate estimations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.969

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 studies0.0000.003
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 head0.004
GPT teacher head0.245
Teacher spread0.241 · 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