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Record W6929564146 · doi:10.5061/dryad.280t270

Data from: Belowground competition in forest and prairie

2019· dataset· en· W6929564146 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionWindageExclosureHyporeflexiaFusible alloyTSG101

Abstract

fetched live from OpenAlex

The hypothesis that the intensity of belowground competition varies with community standing crop was tested in forest and prairie in central Canada. Three plots were studied in each habitat. Forest had significantly higher soil nitrate, ammonium, water, and root biomass than prairie, but significantly lower root:shoot ratios. Transplants of a grass (Bouteloua gracilis) and a tree (Populus deltoides) were grown singly for one summer in both habitats with live neighbor roots either absent or present. Live neighbor roots were removed by cutting roots along the perimeter of a 10 cm diameter circle to a depth of 15 cm and then inserting a plastic tube (10 cm diameter, 15 cm long) vertically into the soil. Transplants grown with live neighbor roots also had neighbor roots cut along the perimeter of a 10 cm diameter circle, but no tube was installed. There were ten replicates of each combination of species and root treatment in each plot. The aboveground biomass of transplants showed a significant interactive effect between habitat and competition treatment. Biomass was significantly lower in the presence of neighbor roots in prairie, but not in forest. Neighbor roots significantly decreased the survivorship of the tree but not the grass. Belowground competition was most intense in prairie, where the supply of soil resources was lowest.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.178
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.012

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 head0.063
GPT teacher head0.290
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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