MétaCan
Menu
Back to cohort
Record W2168378299 · doi:10.21273/hortsci.46.2.260

Ethylene Exposure Duration Affects Postharvest Needle Abscission in Balsam Fir (Abies balsamea L.)

2011· article· en· W2168378299 on OpenAlexaffabout
Mason T. MacDonald, Rajasekaran R. Lada, Alex Martynenko, Martine Dorais, Steeve Pépin, Yves Desjardins

Bibliographic record

VenueHortScience · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsUniversité LavalNova Scotia Department of AgricultureAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAbscissionBalsamEthyleneAbies balsameaPostharvestHorticultureToxicologyBotanyBiologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

Needle loss after harvest is a major problem for Atlantic Canada's Christmas tree and greenery industry. Ethylene is a signal for abscission in balsam fir, but preliminary studies have suggested that the role of ethylene may be influenced by length of exposure. Short-term and long-term ethylene exposure experiments were conducted. Branches were exposed to ethylene for 24 h (short-term) or continuously (long-term) at concentrations of 0 to 1000 ppm. The response variables measured were needle retention duration (NRD), average water use (AWU), and xylem pressure potential (XPP). Short-term exposure to any concentration of ethylene delayed needle abscission by 30 to 40 days. In contrast, long-term exposure to all concentrations of ethylene accelerated abscission, most evident by a 21-day decrease in NRD at 1000 ppm ethylene. There was a 60% decrease in NRD, 160% decrease (more negative) in XPP, and 80% increase in AWU as a result of long-term exposure to ethylene. Overall, our results demonstrate an opposite effect of short-term and long-term ethylene exposure, which suggests that short-term exposure to ethylene might help to precondition balsam fir and delay needle abscission during postharvest handling.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.224
Teacher spread0.172 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueHortScienceSame topicPostharvest Quality and Shelf Life ManagementFrench-language works237,207