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Lake Ecosystems

2014· other· en· W4246855928 on OpenAlexaff
Nelson G. Hairston, Gregor F. Fussmann

Bibliographic record

VenueEncyclopedia of Life Sciences · 2014
Typeother
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiogeochemical cycleEcosystemWatershedEnvironmental sciencePhytoplanktonFood chainLake ecosystemEcologyAquatic ecosystemFood webBiogeochemistryNutrientSurface waterBiologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Lakes are bodies of nonmarine standing water connected by water flow and aerial inputs to their surrounding landscapes (watersheds). As relatively discrete ecosystems, the interplay between physical, biogeochemical and organismal processes in them is especially clear, and can be studied, understood and put to use in effective management. Sunlight penetrating from the lake surface provides energy that warms the surface water, energy for photosynthesis and an environment suitable for predators that hunt by sight. The depth to which light penetrates is determined by the amount of suspended particles (phytoplankton, organic and inorganic sediments) and coloured organic chemical compounds dissolved in the water. Important chemicals entering from the watershed include essential nutrients (chiefly phosphorus and nitrogen) and pollutants that are taken up and passed through the food chain from primary producers (phytoplankton and rooted plants) to consumers (animals that eat plants and other animals). All organisms in lakes have adaptations that affect the strengths of their interactions with their physical and biogeochemical environments and with other species in the food web. Introduced species, pollutants, and other changes in the environment result in rapid evolution of the adaptations that determine interaction strengths. These processes are particularly obvious in discrete lake ecosystems. Key Concepts: Lakes are relatively discrete ecosystems; the interplay between physical, biogeochemical and organismal processes in them can be easily studied. Lakes take up a small proportion of the Earth's surface but their ecological importance is disproportionately high. Lake ecosystems are influenced by their watersheds; a lake and its watershed are often considered to be a single ecosystem. Thermal stratification in lakes generates vertical structure and compartments with different physical, chemical and biological properties. The shallow‐water littoral and the open‐water pelagic are the two major horizontal zones in lakes; each zone has its characteristic food chain based on macrophytes and benthic algae or phytoplankton. Carbon, nitrogen and phosphorus are the major nutrients affecting lakes and their watersheds as part of their biogeochemical cycles. Production is limited by phosphorus in most, but not all, lakes. The lake sediment plays an important role as habitat for rooted plants and animals, as nutrient storage (particularly phosphorus), and as a repository of decayed material and dormant stages of lake organisms. Both bottom‐up and top‐down processes determine the trophic structure and dynamics in lake food chains and webs. Lake ecosystems are shaped by both ecological and evolutionary processes that occur on the same time scale.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.003

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.

Opus teacher head0.008
GPT teacher head0.218
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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