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Record W2160582543 · doi:10.1139/b2012-025

Tiller organization within the tussock grass <i>Schizachyrium scoparium</i>: a field assessment of competition–cooperation tradeoffs

2012· article· en· W2160582543 on OpenAlexvenueno aff
Justin Derner, David D. Briske, H. Wayne Polley

Bibliographic record

VenueBotany · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersAgricultural Research Service
KeywordsTussockBiologyTiller (botany)Competition (biology)AgronomyEcologyBotany

Abstract

fetched live from OpenAlex

Tussock grasses are characterized by a compact spatial arrangement of tillers that contributes to intense intratussock competition. This investigation was designed to directly assess the magnitude of competition among autonomous subunits of tillers within individual tussocks (i.e., integrated physiological units) to further define the mechanisms of tiller organization within this successful growth form. Experimentally constructed tussocks of Schizachyrium scoparium (Michx. Nash) were grown in the field in central Texas, USA, for 2 years with 1, 4, 8, or 12 autonomous subunits to span the range observed in naturally occurring local populations. Increasing numbers of subunits per tussock did not affect tiller density or the mean mass of individual vegetative or reproductive tillers, but it did intensify intratussock competition as evidenced by a large reduction in total tiller mass and number per subunit. This pattern of tiller organization is indicative of a division of labor within the tussock that is manifested as a tradeoff between competition among autonomous subunits and cooperation among tillers within these physiologically integrated subunits. We conclude that an increasing number of autonomous subunits associated with tussock basal expansion contributes to coarse-scale resource preemption and competitive ability, while resource sharing among tillers within subunits supports new tiller establishment and growth within this highly competitive microenvironment.

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 score0.041
Threshold uncertainty score0.807

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 head0.008
GPT teacher head0.238
Teacher spread0.231 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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