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Record W2886154306 · doi:10.1101/391748

Biological control protects tropical forests

2018· preprint· en· W2886154306 on OpenAlexaff
Kris A. G. Wyckhuys, Alice C. Hughes, C. Buamas, Anne C. Johnson, Liette Vasseur, Louis Reymondin, Jean‐Philippe Deguine, Douglas Sheil

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicBiological Control of Invasive Species
Canadian institutionsBrock University
Fundersnot available
KeywordsMealybugDeforestation (computer science)AgroforestryCroppingGeographyCropBiologyEcologyForestryAgricultureHemiptera

Abstract

fetched live from OpenAlex

Abstract Biological control of invasive species can restore crop yields, and thus ease land pressure and contribute to forest conservation. In this study, we show how biological control against the mealybug Phenacoccus manihoti (Hemiptera) slowed deforestation across Southeast Asia. In Thailand, the newly-arrived mealybug caused an 18% decline in cassava yields over 2009-2010, a shortfall in national production and an escalation in the price of cassava products. This spurred an expansion of cassava cropping in neighboring countries from 713,000 ha in 2009 to >1 million ha by 2011: satellite imagery reveal 388%, 330%, 185% and 608% increases in peak deforestation rates in Cambodia, Lao PDR, Myanmar and Viet Nam focused in cassava crop expansion areas. Following release of the host-specific natural enemy Anagyrus lopezi (Hymenoptera) in 2010, mealybug outbreaks were reduced, cropping area contracted and associated deforestation slowed by 31-94% in individual countries. When used with due caution and according to current guidelines, biological control offers broad benefits for people and the environment.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.207
Teacher spread0.181 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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