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Record W3196281744 · doi:10.1111/nph.17685

Novel chemicals engender myriad invasion mechanisms

2021· review· en· W3196281744 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Phytologist · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsUniversity of Alberta
FundersUniversity of TennesseeNational Science Foundation
KeywordsBiologyEcologyComputational biology

Abstract

fetched live from OpenAlex

Non-native invasive species (NIS) release chemicals into the environment that are unique to the invaded communities, defined as novel chemicals. Novel chemicals impact competitors, soil microbial communities, mutualists, plant enemies, and soil nutrients differently than in the species' native range. Ecological functions of novel chemicals and differences in functions between the native and non-native ranges of NIS are of immense interest to ecologists. Novel chemicals can mediate different ecological, physiological, and evolutionary mechanisms underlying invasion hypotheses. Interactions amongst the NIS and resident species including competitors, soil microbes, and plant enemies, as well as abiotic factors in the invaded community are linked to novel chemicals. However, we poorly understand how these interactions might enhance NIS performance. New empirical data and analyses of how novel chemicals act in the invaded community will fill major gaps in our understanding of the chemistry of biological invasions. A novel chemical-invasion mechanism framework shows how novel chemicals engender invasion mechanisms beyond plant-plant or plant-microorganism interactions.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.117
GPT teacher head0.320
Teacher spread0.203 · 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