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Record W1561539297 · doi:10.26786/1920-7603(2014)6

Introduction to Special Issue of the Journal of Pollination Ecology on Crop Pollination Deficits

2014· article· en· W1561539297 on OpenAlexvenueno aff
Barbara Gemmill‐Herren, Koos Biesmeijer

Bibliographic record

VenueJournal of Pollination Ecology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinationPollinatorAgricultureEcosystem servicesBiologyFood securityCropAgroforestryEcologyDocumentationEcosystemPollen

Abstract

fetched live from OpenAlex

While it is well recognised that pollination is an ecosystem service of vital importance to human well-being through its role in food production, it is still remarkable how little is known, on a crop-by-crop basis, about this role, and the extent and causes of declines in the service. Without better documentation of the specific contribution of pollination to crop yields, there have been mounting - and justified - questions about how relevant pollination may be to agricultural development and food security. In addition, the vast majority of studies of pollination services to crops have been carried out in Europe and North America; and certainly the problems we know to impact pollinators most severely – a high dependence on agricultural chemicals and monocropped landscapes offering little diet diversity to pollinators – are typical features of industrialised, Northern hemisphere agriculture.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0620.021

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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
Published2014
Admission routes1
Has abstractyes

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