MétaCan
Menu
Back to cohort
Record W1991148974 · doi:10.1080/00173134.2012.659203

Microclimatic-induced fluctuations in the flower and pollen production rate of olive trees (<i>Olea europaea</i>L.)

2012· article· en· W1991148974 on OpenAlexfundno aff
Fátima Aguilera, Luís Ruíz Valenzuela

Bibliographic record

VenueGrana · 2012
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
FundersMount Allison University
KeywordsOleaPollenOlive treesCultivarBiologyPhenologyInflorescenceMicroclimateHorticultureAltitude (triangle)OleaceaeBotanyAgronomyEcologyMathematics

Abstract

fetched live from OpenAlex

The possible impact of altitude and the related microclimatic conditions on the total production of fruiting branches, inflorescences, flowers and pollen grains of olive trees Olea europaea was analysed. A total of 90 Picual cultivar trees, the most extensive olive cultivar in the Iberian Peninsula, were studied for a three-year period (2007–2009). The study shows that production of flowers and pollen grains in a cultivar of the olive tree varies according to the microclimate. Our study also indicates that the olive trees frequently can have up to half a million flowers per tree. Moreover, the total flower production differs between years and study areas. In the Picual cultivar, the average production of pollen grains per anther is usually more than 60 000 grains. The total production of pollen per tree is around 72 000 million on average. The most favourable microclimatic conditions for reproduction in olive trees are found in years and olive growing areas with low temperature and high precipitation records during the months prior to flowering of the olive trees. We hypothesise that olive trees tend to increase their pollen production rate as altitude increases, which can be interpreted as a reproductive strategy to ensure fertilisation.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.260
Teacher spread0.235 · 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 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

Citations33
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueGranaSame topicAllergic Rhinitis and SensitizationFrench-language works237,207