Bibliographic record
Abstract
An ecosystem is composed of various biotic and abiotic components. Although they are connected, each has a unique chemical signature that can characterize that component. The risk assessor is faced with the option of selecting for study and sampling one or more types of media type to characterize the ecosystem of interest. It should be appreciated that although the components are linked and elements flow from one system to another, there is not necessarily a strong correlation between the distribution patterns of elements in one pool and those in another. The distribution of elements in the different media reflects complex interactions between the different components of the ecosystem and the factors that control them ( soil pH, eH, and the content of organic matter). In addition, the levels of element concentration in biological systems are affected by seasonal variations and by the adaptive mechanisms of biota that allow them to maximize their competitive advantage (certain plants are bio-accumulators of specific elements). The risk assessor should consider the variation inherent in each of the media types that is being sampled. For example, within one tree species there are different tissue types (eg. leaf, bark, woody layer) and there may be up to a magnitude of difference in the concentration of elements from one type of tissue to the next (e.g. Ni in sugar maples). Hence, if data from more than one type of tissue are compared, the result may show variation in element concentrations that are not realistic. This workshop focuses on soils and the inherent differences that affect the concentration of metals in soils. Soils have properties that make them a useful sample media for risk assessment. They are present in most places and readily acessible, the seasonal fluctuations in their chemistry are not significant, and they are directly linked to biological uptake. There is variation in the element content of soils with increasing depth caused by the soilforming processes and horizon development. However, if samples are collected from similar pedologic horizons, the origin of the differences tends to be more easily explained.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".