Bibliographic record
Abstract
Fishery resources include the fishes, the other biota they interact with, and the habitats they occupy. The historical sequences in the use and management of these resources may be considered as a series of interacting sequences of change. These sequences can span from social, economic, institutional and landscape changes, through water quality, habitat supply, and climate changes, to biotic composition changes, introductions and extinctions, and species harvest changes. A selection of these sequences is examined for the St. Lawrence-Great Lakes which has been subjected to intense development and study over the last 200 years. While the whole basin is considered, some detailed attention is given to Lake Ontario and, within it, the Bay of Quinte. Brief selective development histories are given for the three areas as context. The management history is outlined and critiqued. While much progress has been achieved in cleaning up the load-driven problems in the basin, little secure progress toward rehabilitation and sustainability has been achieved. In the current period of economic problems, governments, particularly Canada’s, are undoing past ecosystem management progress. The development of St. Lawrence-Great Lakes’ ecosystem science has drawn heavily from both oceanography and limnology. A brief, selective overview of several progressions in ecosystem science illustrates how knowledge and understanding of this ecosystem has expanded over the last 60 years, providing an improved basis for management action. As with use and management, the science of fishery resource management has consisted of many historical progressions. The many sequences in the St. Lawrence-Great Lakes management and science histories lend support for recognition of (i) the importance of taking an ecosystem approach to renewable resource management, (ii) the value of adaptive management practices and, particularly, (iii) the vital complementary roles of long-term monitoring and mathematical modelling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".