MétaCan
Menu
Back to cohort
Record W4392138716 · doi:10.59720/20-132

Analyzing the effect of mycorrhizal fungi on plant communication of nutrients

2024· article· en· W4392138716 on OpenAlexaboutno aff
Aveena Khanderia, Maya Jones, Aanya Khanderia, Jillian Varner

Bibliographic record

VenueJournal of Emerging Investigators · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMycorrhizal fungiNutrientBiologyBotanyAgronomyEcologyHorticultureInoculation

Abstract

fetched live from OpenAlex

The goal of our experiment was to determine if phosphate transfer occurred between plants, using mycorrhizal fungi. We hypothesized that a “communication” network existed between plants when the mycorrhizal fungi were present and that there would be no transfer of nutrients if the mycorrhizal fungi were not present. This study extended the analysis of Suzanne Simard who studied Paper Birch, Douglas Fir, and Western Red Cedar in Canada. She was able to confirm that there is a massive underground communication where different plants cooperate with resources using mycorrhizal fungi. The purpose of our experiment was to see if plants with nutrients would transfer their excess levels of phosphate to the plant that had a limited amount of nutrients. Overall, no definitive conclusion could be drawn from the treatments conducted. The measurements greatly deviated from the hypothesized results and demonstrated a more complicated relationship than originally thought. Further study will need to be conducted to determine if there are further conditions that must be met, such as a minimum amount of phosphate concentration or a minimum differential between the resources (in terms of this study, phosphate concentration) of the two plants, for this transfer to occur. The results of this study, if replicated and shown to be conclusive at long distances, could be used to aid forest management.

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)0.001
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 score0.397
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Emerging InvestigatorsSame topicMycorrhizal Fungi and Plant InteractionsFrench-language works237,207