Bibliographic record
Abstract
One purpose c fthis article isto synthesize some recentresults on the dynamics of mathematical models of chemically stressed aquatic populations and communities; in particular, we (1) illustrate some of the difficulties that might arise from extrapolation of bioassay results to dynamic, chemically stressed population and community models; and (2) indicate different ways in which chemicals can affect the dynamics of population models. Bioassays, an important component of ecological impact and risk assessment, can be. misleading if extrapolated to settings beyond experimental boundaries. Extrapolation of bioassays to the populations and community levels can not be direct because derived information is usually specific for a subset of individuals and obtained under experimental constraints on time and parameters. We present examples derived from a mathematical setting where consequences of bioassays, even when employed as the fundamental determinant of stress in the systeni, have no predictable relationship to the ultimate effect of the chemical on the system. The first illustration, at the population level, demonstrates that sublethal effects of a lipophilic chemical with a reversible mode of action on individuals attained at concentrations well below the LC50, indeed even below the EC50 for growth, can drive the population to extinction so that the chemically stressed population is much more severely damaged than predicted by hioassays. The second illustration at the community level indicates that results of bioassays can also indicate outcomes that (ire worse than actually occurs for the community. Finally, we compare the outcome of a spectral analysis oftime series of a sequence of chemically stressed populations, demonstrating that complex effects of lipophilic chemicals on population dynamics are not readily identifiahle from spectral signatures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.988 | 0.989 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".