Bibliographic record
Abstract
Human population and associated industrial activities continue to increase rapidly, and have reached levels that put the environment under stress in many areas of the world. In addition natural fluctuations of the Earth's physical and biological systems, often occur in time frames that are not readily evident to man. Such fluctuations cause additional stress on the environment, and can result in changes that impact society in terms of diminished availability of clean water, unspoiled land and natural vegetation, minerals, fish stocks, and clean air. Human societies are making a rapidly increasing number of policy and management decisions that attempt to allow both for natural fluctuations and to limit or modify human impact. Such decisions are often ineffective, as a result of economic, political and social constraints, and inadequate understanding of the interactions between human activities and natural responses. Improved understanding of such issues is important in its own right, and will contribute to ameliorating economic, political and social constraints. Developing improved understanding of environmental change is within the realm of the natural sciences and is being addressed by the International Geosphere-Biosphere Programme (IGBP) and other programmes concerned with describing and understanding the Earth System. Natural variability, occurring over a variety of time scales, dominates the health of complex marine ecosystems, regardless of fishing or other environmental pressure. We are only now beginning to compile quantitative documentation of such variability, and consequently our knowledge concerning its causes remains at the level of hypotheses. Understanding of the role of variability in the functioning of marine ecosystems is essential if we are to effectively manage global marine living resources such as fisheries during this period of tremendously increased human impact, and concurrent dependence, on these resources.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.991 | 0.994 |
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".