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Record W6926588751 · doi:10.21966/3nm8-av33

Stream Event Sampling - Calvert Island - 2015-2018

2015· dataset· en· W6926588751 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHakai Institute · 2015
Typedataset
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsHydrographWatershedHydrology (agriculture)STREAMSSampling (signal processing)StormEvent (particle physics)Streamflow

Abstract

fetched live from OpenAlex

The Stream Event Sampling survey is a part of the Kwakshua Watersheds Program. It was designed to investigate the change in river water chemistry (with a focus on Dissolved Organic Carbon) with changing river flow associated with a rain event. On the one hand, the goal was to collect high flow water samples for a wide range of rain events and across seasons for the seven focal stream outlets of the Kwakshua Watersheds Program. For each rain event, a single high flow sample has been selected and used in the estimate of dissolved organic carbon flux from these watersheds (Oliver et al., 2017). On the other hand, the goal was to collect a detailed sequence of stream water samples along the rising and falling limbs of the storm hydrographs of the seven focal streams. Two stream event surveys were conducted in great detail for a late summer and early fall event at all seven watersheds in 2014 . In addition several discrete high flow samples were taken during that summer (2014 data available here: https://doi.org/10.21966/ywbk-5h57). In summer 2015, one rain event was captured entirely by stream event samples for all seven watersheds. After installation of the pump sampler at watershed 708, several rain events were sampled at that location in the fall of 2015. In 2016, only discrete high flow samples were collected for two rain events in April. In 2017, two full rain events were sampled at all watersheds in May and in July. Additionally, discrete high flow grab samples were taken in June and August. Several full rain events were sampled in the fall of 2017 with the autosampler at watershed 708. In 2018, only discrete high flow samples from the watershed outlets were taken at several rain events throughout the year. High flow water samples along the rising limb were taken with the aid of an automatic rack sampler. This is a mechanical device that samples water at predefined water stages. Several bottles are mounted at vertical increments above low water level. As the water level rises, bottles fill in sequence. Each bottle has an intake device that allows it to fill curing the rise while preventing further exchange of sample water with the stream. The exact water stages are determined with the aid of an Odyssey water stage logger installed nearby. In fall 2015, an automatic electronic pump sampler was installed at watershed 708, which allowed us to remotely (or programmatically) trigger the collection of stream samples. Samples taken prior to a rain event and along the falling limb of an hydrograph were taken manually. These three distinct sample methods are named Rack Sample (RS), Auto Sample (AS) and Grab Sample (GS). Sample water was analyzed for SUVA from Hakai Institute; Dissolved Organic Carbon and Alkalinity, cations and anions from North Road Analytical Laboratory; DO13C from GGHATCH; Total Dissolved Nitrogen, Total Nitrogen, Total Dissolved Phosphorous, Total Phosphorous, NH4 and O18/H2 isotopes from University of Alberta; NO3, SiO2 and PO4 from UBC; Particular Organic Matter from UC Davies; Specific Conductance, temperature, pH, ORP and DO from in situ collection with a YSI. Sample metadata includes the time of collection (Pacific Standard Time), the time a sample was retrieved from either the rack sampler or the autosampler, the time of sample preservation and filtration and the date of sample analysis. It also includes a ‘sampling bout’, which indicates the chronological order of a set of samples that were taken during the same storm event at the same watershed. Further on the sampling method is indicated (RS, AS or GS). Please consult the ‘STR_data_dictionary_2015-2018’ file for a detailed description of all variables and acronyms used as column headers and flags, including units for each variable. Samples collected in 2014 only includes Dissolved Organic Carbon and SUV absorbance samples. Sample ID labels do not correspond with current Hakai ID’s and are therefore not stored in the Hakai data portal. Therefore, these data are stored in a separate spreadsheet (STR data package 2014 v1.21.xlsx) in the following data package: https://doi.org/10.21966/ywbk-5h57 The data collected in 2014 have been analyzed and reported in the MSc thesis of M.C. Korver (2015). The data of 2015 and beyond are here available but have yet to be analyzed. This dataset is near completion: POMS data collected in April, August and November 2018 is awaiting processing at UC DAVIES and O18 data collected in November 2018 is awaiting analysis at the University of Alberta.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.319
Teacher spread0.275 · 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