Bibliographic record
Abstract
Abstract Lake ice supports a range of socio‐economic and cultural activities including transportation and winter recreational actives. The influence of weather patterns on ice‐cover dynamics of temperate lakes requires further understanding for determining how changes in ice composition will impact ice safety and the range of ecosystem services provided by seasonal ice cover. An investigation of lake ice formation and decay for three lakes in Central Ontario, Canada, took place over the course of two winters, 2015–2016 and 2016–2017, through the use of outdoor digital cameras, a Shallow Water Ice Profiler (upward‐looking sonar), and weekly field measurements. Temperature fluctuations across 0°C promoted substantial early season white ice growth, with lesser amounts of black ice forming later in the season. Ice thickening processes observed were mainly through meltwater, or midwinter rain, refreezing on the ice surface. Snow redistribution was limited, with frequent melt events limiting the duration of fresh snow on the ice, leading to a fairly uniform distribution of white ice across the lakes in 2015–2016 (standard deviations week to week ranging from 3 to 5 cm), but with slightly more variability in 2016–2017 when more snow accumulated over the season (5 to 11 cm). White ice dominated the end‐of‐season ice composition for both seasons representing more than 70% of the total ice thickness, which is a stark contrast to Arctic lake ice that is composed mainly of black ice. This research has provided the first detailed lake ice processes and conditions from medium‐sized north‐temperate lakes and provided important information on temperate region lake ice characteristics that will enhance the understanding of the response of temperate lake ice to climate and provide insight on potential changes to more northern ice regimes under continued climate warming.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".