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Record W4385810185 · doi:10.1080/21580103.2023.2241497

The behavior of insect pollinators in a teak ( <i>Tectona grandis</i> L. f.) clonal seed orchard with weedy understory in East Java

2023· article· en· W4385810185 on OpenAlexaff
Endah Retno Palupi, Sudarsono Sudarsono, Sjamsoe’oed Sadjad, Dedy Duryadi Solihin, John N. Owens

Bibliographic record

VenueForest Science and Technology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Victoria
FundersLembaga Ilmu Pengetahuan Indonesia
KeywordsPollinatorUnderstoryBiologyPollinationForagingOrchardInsectPollenTectonaBotanyEcologyCanopy

Abstract

fetched live from OpenAlex

Teak is an insect-pollinated species, and seed production depends on pollinators. The objectives of this study were to determine potential pollinators of teak and their foraging behavior in obtaining alternate food sources as the basis for management recommendations. Four traps, i.e. Moczarsky-Winkler selector traps, sticky traps, yellow-pan traps, and manual traps (insect net), were put up among blooming inflorescences during the flowering period of March to May. Insects were collected and counted weekly and identified. The presence and location of the pollen on their bodies were observed. The time and duration of foraging behavior, the duration of a single visit, the number of visits in an hour, and the landing position when approaching the teak flower as well as the understory were also observed. The result showed Ceratina sp., and Braunsapis sp. (Apidae), Nomia sp. (Halictidae) were potential teak pollinators in the CSO in East Java. The insect foraging behavior supports the ability to deposit pollen onto the stigma. The presence of Mimosa pudica in the understory plays as an alternate food source for pollinators. Intensive weeding and trimming of old or dead branches should be less practiced to enhance pollinators’ populations and nesting sites.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.200
Teacher spread0.175 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

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