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Dry matter content as a measure of dry matter concentration in plants and their parts

2002· article· en· W2143008070 on OpenAlexafffund
Bill Shipley, Thi‐Tam Vu

Bibliographic record

VenueNew Phytologist · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHerbaceous plantDry matterDry weightSpecific leaf areaBotanyBiologyAgronomyHorticulturePhotosynthesis

Abstract

fetched live from OpenAlex

Summary This study compared the predictive ability of dry matter content (DMC, dry mass per fresh mass) of leaves, stems, roots and entire plants in relation to dry matter concentration (D, dry mass per volume of plant organ). Data came from 28 species of field‐collected plants (woody and herbaceous) and 17 species of herbaceous plants grown in hydroponic sand culture. Specific leaf areas were also measured. Dry matter content of the herbaceous plants grown in sand culture varied more between tissue types than did dry matter concentration but the correlation among plant parts was stronger when using DMC. Means and standard errors for DMC (g g −1 ) were 0.212 ± 0.009 (leaves), 0.176 ± 0.012 (support tissues) and 0.170 ± 0.021 (roots); for D (g cm −3 ) the values were 0.158 ± 0.010 (leaves), 0.168 ± 0.017 (support tissues) and 0.153 ± 0.013 (roots). Leaf DMC provided approximate estimates of leaf D ( r = 0.76) for the field‐collected plants but sclerophyllous leaves from shrubs restricted to acidic bogs proved to be outliers. The relationship between these two variables was stronger in the herbaceous species grown in sand culture, especially so for whole plant estimates ( r = 0.91). Dry matter content and dry matter concentration were equally good predictors of specific leaf area.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.024
GPT teacher head0.203
Teacher spread0.179 · 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.

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

Citations240
Published2002
Admission routes2
Has abstractyes

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