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Record W2766471013 · doi:10.2135/cropsci2017.03.0197

Minimum Daily Respiration of Maize: Relationship to Total Daily Respiratory Carbon Loss, and Effects of Growth Stage and Temperature

2017· article· en· W2766471013 on OpenAlexaff
J. A. Di Matteo, Katelyn E. Goldenhar, Helena Earl

Bibliographic record

VenueCrop Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRespirationAnimal scienceMorningRespiration rateBiologyBotany

Abstract

fetched live from OpenAlex

ABSTRACT Since crop respiration can only be measured in darkness, estimating total daily crop respiration (Rt) requires knowledge of the quantitative relationship between daytime and nighttime respiration, including the predicted effect of temperature. We measured minimum daily (early morning) respiration (Rmin) in maize (Zea mays L.) at four different temperatures during the night and at four different growth stages to estimate the respiration response to temperature. In a field experiment, respiration was measured every 2 to 5 h over a 24‐h period at five different growth stages to explore the relationship between Rt and Rmin. The fractional rate change with 10°C temperature increment (Q10) decreased as the temperature increased. A single function was proposed to describe the respiration response to temperature across all growth stages. The minimum respiration of the day was reached between midnight and 6:00 AM, and Rmin represented 89 to 57% of Rt. Modeling maize respiration assuming a constant Q10 is inadvisable, since Q10 varies with temperature. There was no effect of growth stage on Q10 until 26°C; above this temperature, respiration increased only in older plants. The Rt increased over the season as biomass accumulated, but the ratio of Rmin/Rt reached its highest value at silking.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.229
Teacher spread0.221 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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