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Record W6948198221 · doi:10.5061/dryad.6hdr7sr3b

Patterns of pollen dispersal and pollen capture in the hybridizing cattails, Typha latifolia and T. angustifolia

2022· dataset· en· W6948198221 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Image and Video Retrieval Techniques
Canadian institutionsTrent University
Fundersnot available
KeywordsPollenBiological dispersalTypha angustifoliaTyphaPollen source

Abstract

fetched live from OpenAlex

Pollen dispersal regulates the formation of the invasive, wind-pollinated hybrid cattail T. × glauca, the F1 offspring of the broadleaf (T. latifolia) and narrowleaf (T. angustifolia cattail. An earlier study suggested that pollen dispersal by T. latifolia might be spatially restricted, with most dispersal occurring over distances less than 2 m. Restricted pollen dispersal would imply that hybrid formation primarily occurs within mixed stands of cattails. Hybrid formation might also be affected by preferential receipt of conspecific pollen, but this has not been investigated for cattails. We compared patterns of pollen dispersal for T. latifolia and T. angustifolia using a wind tunnel. We then tested whether patterns of pollen receipt were biased toward the capture of conspecific versus heterospecific pollen using monospecific cattail stands with a single local pollen source. Results from the wind tunnel partially supported the previous finding of spatially restricted pollen dispersal for T. latifolia, the paternal parent of F1 hybrids. Pollen receipt by T. angustifolia was biased toward the capture of conspecific pollen. Localized pollen dispersal by T. latifolia and preferential conspecific pollen capture by T. angustifolia should reduce rates of hybrid formation below that expected under random mating.

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)0.001
metaresearch head score (Gemma)0.001
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicAdvanced Image and Video Retrieval TechniquesFrench-language works237,207