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Record W1995157185 · doi:10.1126/science.1189736

Plants Integrate Information About Nutrients and Neighbors

2010· article· en· W1995157185 on OpenAlexaff
James F. Cahill, Gordon G. McNickle, Joshua J. Haag, Eric G. Lamb, Samson M. Nyanumba, Colleen Cassady St. Clair

Bibliographic record

VenueScience · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsNutrientEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Animals regularly integrate information about the location of resources and the presence of competitors, altering their foraging behavior accordingly. We studied the annual plant Abutilon theophrasti to determine whether a plant can demonstrate a similarly complex response to two conditions: presence of a competitor and heterogeneous resource distributions. Individually grown plants fully explored the pot by using a broad and uniform rooting distribution regardless of soil resource distributions. Plants with competitors and uniform soil nutrient distributions exhibited pronounced reductions in rooting breadth and spatial soil segregation among the competing individuals. In contrast, plants with competitors and heterogeneous soil nutrient distributions reduced their root growth only modestly, indicating that plants integrate information about both neighbor and resource distributions in determining their root behavior.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.878

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 communication0.0000.001
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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 designBench or experimental
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

Citations341
Published2010
Admission routes1
Has abstractyes

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