Additional file 1 of Compost, plants and endophytes versus metal contamination: choice of a restoration strategy steers the microbiome in polymetallic mine waste
Bibliographic record
Abstract
Additional file 1. Supplementary data and results. Figure S1. qPCR data expressed as bacterial (16S rRNA) gene copy number and fungal (28S rRNA) gene copy number (log-transformed), and the ratio of bacteria:fungi for pre-treatment materials and ending treatments for both compost and tailing layers (collected after 56 days; 57 days; and 58 days of incubation in the case of TP and TPC; TPE and TPEC; and T and TC treatments, respectively). Ending treatments included tailings (T; n = 8), tailings with added compost (TC, n = 8), tailings with a plant (TP, n = 13), tailings with a plant and added compost (TPC, n = 8), tailings with a plant inoculated with endophytes (TPE, n = 11) and tailings with a plant inoculated with endophytes and added compost (TPEC, n = 8). Pre-treatment materials included initial compost (Cin, n = 4) and initial tailings (Tin, n = 6). Table S1. General characterization and total trace element content in pre-treatment materials: Blue Nose mine tailings amended with dolomite (Tin) and compost (Cin). Table S2. The plant growth-promoting properties of endophytes used for the inoculation of B. curtipendula seeds. Table S3. Pairwise comparison of prokaryotic (a) and fungal (b) Shannon diversity indices in tailings, compost, and roots between treatments. Table S4. Pairwise comparison of prokaryotic (a) and fungal (b) community structure in tailings, compost, and roots of B. curtipendula between treatments.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.855 | 0.163 |
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".