Using the potential of Canadian goldenrod (Solidago canadensis) as a functional filler with antioxidant activity of low-density polyethylene composites as an example of sustainable development of an invasive plant
Bibliographic record
Abstract
This dataset contains source data obtained during research under the project UMO-2021/43/D/ST8/01491, "Research on the influence of the polymer composites processing conditions on the stabilizing effect of functional plant-derived fillers", relating to research results described in the publication "Using the potential of Canadian goldenrod (Solidago canadensis) as a functional filler with antioxidant activity of low-density polyethylene composites as an example of sustainable development of an invasive plant"; DOI: 10.1016/j.indcrop.2024.119509.As part of the research work carried out, measurements were carried out using the following methods:- FTIR - Fourier transform infrared spectroscopy- OM - optical microscopy| abbreviation: - TGA - thermogravimetry TGA- OIT - oxygen induction time raw data from DSC- DSC - differential scanning calorimetry DSC- PSD - particle size distribution based on microscopy analysis- TENSILE - static tension test- COLOR - colorimetric analysis- SEM - scanning electron microscopyData contains a readme.txt file containing essential information about sample assignment and measurement methods. Please consult the readme.txt file for additional information.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.107 | 0.061 |
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".