Bibliographic record
Abstract
European settlement of the Toronto area began with the establishment of York as the new capital of Upper Canada in 1793; it would later be incorporated as the City of Toronto in 1834 (City of Toronto, 2006). Wholesale water extractions from Toronto Harbour began in 1841 with the construction of the first water system and by 1877, pollution in the harbour was so severe that public water is now drawn from the open lake (Richardson, 1980).Toronto Harbour has a long history of anthropogenic issues, leading to ecological studies. Eutrophication emerged as a major public health concern in the 1960s (Vollenweider, 1968) and was especially apparent on the nearshore areas of the lower Great Lakes, including Toronto Harbour (Nalewajko, 1966; Stadelmann and Munawar, 1974). Early studies of the biological communities showed the extent of the problem. In 1977, for example, Haffner et al. (1982) reported total phosphorus concentrations in Toronto Harbour ranging from 25–33 µg l−1, along with algal blooms in mid to late summer. More recently, several studies were carried out to assess the direct impacts and toxicity of sewage outflows and sediment contaminants on the microbial/planktonic communities and productivity in the harbour ecosystem compared to offshore communities (Munawar et al., 1989a,b, 2003; Munawar and Munawar, 2003).The rapid development, urbanization and industrialization of the Toronto and Region watershed, now home to 4 million people, increased the severity and scope of water quality problems in the harbour. It was during 1985 that the concepts of Areas of Concerns (AOCs) and Beneficial Use Impairments were introduced for developing remedial action plans (IJC, 1985; 1987; Hartig and Zarull, 1992). Since its inclusion as one of the AOCs over 30 years ago, the Toronto and Region Remedial Action Plan (RAP) has actively embarked on the mission of improving water quality conditions, combating eutrophication and enhancing the ecosystem health of the waterfront (e.g. Toronto and Region Remedial Action Plan, 1989; Kidd, 2016).The AEHMS has considerable experience in publishing special issues on Areas of Concern. Recent publications have included two issues about the ecosystem health and recovery of Bay of Quinte (AEHMS, 2011, 2012), as well as two issues concerned with the ecosystem health, remediation and restoration of Hamilton Harbour (AEHMS, 2016, 2017). This current issue is the fifth AOC publication which brings together a large body of up-to-date data and information about Toronto and Region Remedial Action Plan. The issue consists of 12 manuscripts discussing physical/chemical regimes, remediation of Beneficial Use Impairments, and habitat restoration, as well as providing a synthesis and highlights. We hope that this special issue will be beneficial to researchers, managers and students involved in the remediation and restoration of Great Lakes Areas of Concern.I would like to take this opportunity to thank the special issue editorial committee listed below.The financial support of Toronto and Region Conservation Authority is acknowledged towards publication of this special issue. Thanks to Mark Fitzpatrick of Fisheries & Oceans Canada for providing editorial assistance. The support of AEHMS (J. Lorimer, S. Blunt, L. Elder, R. Rozon) in processing and technical editing of the manuscripts is much appreciated.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.348 | 0.187 |
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".