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Record W2038425323 · doi:10.1080/00103620009370479

Elemental content relationships in greenhouse grown apple seedlings supplemented with copper and peat

2000· article· en· W2038425323 on OpenAlexaff
Milan Ihnat, G.H. Neilsen, E. J. Hogue

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBariumPeatStrontiumChemistryManganesePotassiumZincRubidiumCopperPhosphorusSodiumMagnesiumMolybdenumEnvironmental chemistryInorganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract Apple seedlings from a greenhouse pot experiment, investigating the effects of copper (Cu) and peat amendments on crop performance growing on a Cu‐and potassium (K)‐deficient soil, were characterized for a range of major and trace elements. Concentrations of barium (Ba), calcium (Ca), Cu, iron (Fe), K, magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), rubidium (Rb), strontium (Sr), and zinc (Zn) in leaf and stem tissues were correlated with treatment and tissue. Addition of Cu, peat and the nature of the tissue had significant impacts on many element concentrations. Generally, increasing Cu resulted in elevated Ba, Fe, Mo, and Sr as well as Cu levels. The presence of peat resulted in reduced levels, generally in both leaf and stem, of Ba, Mg, Mn, Rb, and Zn and increased levels of Fe, K, and Mo. Finally, the vast majority of elemental concentrations were higher in leaf tissue rather than stem, with the exceptions of Na and Zn. Elemental concentration ranges, over all tissues and conditions of added Cu and peat were (mg kg‐1) Ba 9–49, Ca 6380–16340, Cu 2–11, Fe 10–57, K 4070–16950, Mg 900–4260, Mn 22–197, Mo 0.02–0.19, Na 28–124, Rb 0.7–12, Sr 41–58, Zn 18–48.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.070
GPT teacher head0.253
Teacher spread0.183 · 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

Citations4
Published2000
Admission routes1
Has abstractyes

Explore more

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