Bibliographic record
Abstract
Habitat science can provide the unifying concepts to bring together ecological studies of physiological tolerances, predator avoidance, foraging and feeding, reproduction and life histories. Its unifying role is built on two assumptions, imported from terrestrial habitat science and not always stated explicitly: that competition is present interspecifically and intraspecifically under at least some conditions, and that habitat features have some persistence and predictability in space and time. Consistent with its central conceptual position in ecology, habitat science has contributed importantly to scientific advice on pollution, coastal zone management and many other areas of environmental quality, although it has been largely divorced from developments in fish populations dynamics done in support of fisheries management. Commitments by most management agencies to apply an integrated, ecosystem approach to management of human activities in marine systems, poses new challenges to marine science advisors to management. Integrated management and ecosystem approaches both inherently require spatial thinking and spatial tools, making habitat science a particularly relevant advisory framework, particularly because of the unifying role of habitat in ecology. The basic mechanisms behind ocean biological dynamics, productivity, concentration and retention, however, present much weaker opportunities for competition and less persistence and predictability, weakening the foundations of theory and concepts behind current habitat science. The paper highlights the new types of thinking about ‘habitat’ that will be required, if habitat science is to meet the advisory needs of the new approaches to management.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".