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Record W2054433410 · doi:10.4141/s05-012

Wheat root dynamics as affected by landscape position

2006· article· en· W2054433410 on OpenAlexaffvenue
R. M. A. Block, K.C.J. Van Rees

Bibliographic record

VenueCanadian Journal of Soil Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSowingPosition (finance)Environmental scienceProductivitySoil waterAgronomyMathematicsSoil scienceBiology

Abstract

fetched live from OpenAlex

The effects of landscape position on root production and mortality were assessed in a 90-cm-deep soil profile at a shoulder and footslope landscape position seeded to spring wheat (Triticum aestivum L.). Root length was measured over eight sampling dates using a minirhizotron system, and soil water content and temperature were recorded at various depths at each landscape position. The shoulder position was drier than the footslope position in the upper 30 cm due to a greater frequency and duration of soil temperatures > 20°C, and at depth (> 75 cm). Mean root length was greatest at the footslope position and was concentrated in the upper 20 cm of the profile, while the shoulder position had the greatest root length at the 40- to 60-cm depth. Mean daily root production peaked at 5.0 to 6.0 m m -2 d -1 at the 43rd day after planting (DAP) for both landscape positions, which corresponded to the time of booting. Daily rates for root mortality ranged from 0.5 to 2.5 m m -2 d -1 . Soil water content and daily root production at the 10-cm depth were positively correlated at both landscape positions. Information on landscape position differences in root productivity and mortality could help to improve placement of inorganic fertilizers, and estimation of below-ground carbon sequestration. Key words: Wheat, roots, minirhizotron, landscape position

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.182
Teacher spread0.178 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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