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Record W6963082508 · doi:10.17632/5nzvr2g7vk.1

Nectar secretion dynamics in Nicotiana rustica

2022· dataset· en· W6963082508 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNectarAnthesisPollinatorInflorescenceSampling (signal processing)Sampling timePetal

Abstract

fetched live from OpenAlex

Nectar sampling of 23 Nicotiana rustica plants in a 15’ x 15’ garden plot located in Lamont, Alberta (53° 45' 35.7876'' N 112° 46' 38.712'' W) began August 5th, 2020 and took place every day, weathering permitting, until August 20th. Each day, buds on participating plants were bagged with 4" x 6" sheer drawstring pouches to exclude pollinators from the sample flowers. The selected buds were ones that appeared to be near-flowering, so that when eventually sampled they would be in anthesis (Figure 1). Each flower selected was sampled twice in a twelve-hour period, between 8:00 – 10:00 and 20:00 – 22:00. When the initial sample was taken (8:00 – 10:00 or 20:00 – 22:00) alternated each sampling round, so that the final samples of the entire data set would reflect both the nectar produced over the day and overnight. Only one flower/plant/round was sampled to reduce confusion. The initial sample period began by removing the exclusion bags and marking flowers in anthesis on each plant. Nectar samples were then collected from the newly opened flowers with 75 mm Drummond™ Capillary Tubes. The length of the nectar along the tube was measured with a ruler and recorded to be converted to a volume later. After this measurement the nectar was expelled from the microcapillary tube onto the sample well of a Fisherbrand™ Handheld Digital Brix/RI Refractometer, which had been previously zeroed and cleaned with distilled water. In a shaded area with the cover closed, the refractometer read off and produced the average of 15 Brix° measurements of each nectar sample (automatically correcting for temperature), and the number recorded. The sample well was cleaned, the flower re-bagged, and the process repeated for the next marked flower. These same flowers were then resampled 12 hours later using the same process, to determine the volume and Brix° concentration of the nectar produced between sample periods. Hypotheses: H0: Nicotiana rustica’s nectar attributes, including nectar volume (μl), weight of sugar present within the nectar (mg), or the nectar concentration (mg/μl), will not vary depending on the time of day it is secreted. Ha: One or more of Nicotiana rustica’s nectar attributes will vary depending on the time of day it is secreted.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.027

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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