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Record W2397720834 · doi:10.1504/ijbpim.2015.071255

Peer-to-peer mobile data flow in a crop field

2015· article· en· W2397720834 on OpenAlex
Sinh Pham, Richard K. Lomotey, Wen Fu, Ralph Deters

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Business Process Integration and Management · 2015
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceWorkflowRedundancy (engineering)Distributed computingWirelessBloom filterRevenueBandwidth (computing)Computer networkTelecommunicationsDatabase

Abstract

fetched live from OpenAlex

The adoption of mobile technology in the agriculture sector can lead to benefits such as high productivity, improvement in mechanisation, increment in revenue, and timely information accessibility by farmers. The problem, however, is that mobile devices communicate over wireless mediums that can be unreliable; and when data states are kept on distributed nodes, inconsistencies can arise due to inefficiencies in the flow propagation of the updated agronomic data. This work describes a new key-value storage synchronisation workflow using exact set reconciliation and bloom filters. The proposed flow algorithm with its two-phase architect, with the first one being approximate synchronisation and the second one being exact synchronisation, provides better performance regarding bandwidth management. In addition, the usage of redundancy detection in the synchronisation mechanism makes it possible for the proposed algorithm to operate in a peer-to-peer environment, independent of any centralised server.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.046
GPT teacher head0.322
Teacher spread0.276 · 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