Data for: Impacts of large herbivores on mycorrhizal fungal communities across the Arctic
Bibliographic record
Abstract
Data deposited in association with article to be published in the journal: Ecography. Fungal DNA was extracted from soil samples collected at herbivore exclosure experiments in 15 sites around the Arctic to evaluate the impact of large mammalian herbivores on mycorrhizal fungal communities. The data includes DNA sequence reads amplified using ITS1m-LR5 and SSU515fngs-AML2 long-read primer pairs targeting general fungal sequences and arbuscular mycorrhizae, respectively. Sequences were taxonomically assigned through the UNITE database using PlutoF, and the MaarjAM database and assigned to fungal guild using FUNguild. The pipeline from trimmed, demultiplexed sequence reads to taxonomic and functional assignment is included in the Rscript "EcographySubmission_SynthesisScript.R". Soil properties, date of exclosure establishment, and general metadata is included in the file "MetaData.csv", and percent plant functional type cover is included for 11 sites in the file "Cover_data.csv". Raw sequence reads were deposited in the Sequence Read Archive (SRA) under the title "Impacts of large herbivores on mycorrhizal fungal communities across the Arctic".
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.046 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".