That sounds unlikely. It's not how stainless steel works; to bond a coating to the surface of the stainless you have to fight the stainlessness pretty hard.
One of the best indicators for not so stainless stainless steel is a coarse texture to aid in that bonding. Even some fairly expensive brands of vacuum flask exhibit this. Only way to be sure that I know of is a knife.
Generally, the easiest way is with a magnet. The most corrosion-resistant of the common stainless steels are austenitic. These steels are not ferromagnetic, or very weakly ferromagnetic if they have been cold worked. Cheaper and less corrosion-resistant ferritic stainless steels are strongly ferromagnetic.
Martensitic stainless steels are less corrosion resistant than austenitic steels and are ferromagnetic, but they're used for good reason. Unlike other types, martensitic steels are hardenable through heat treatment, making them the only suitable choice for many sharp-edged tools or parts subject to severe abrasion. Stainless steel cutlery is most commonly an 18/0 ferritic grade, but premium cutlery may be an 18/10 austenitic. Kitchen knives are invariably martensitic, because ferritic or austenitic grades wouldn't hold a sharp edge.
"Each text is analyzed by how likely each word would be the predicted word given the context to the left. If the actual used word would be in the Top 10 predicted words the background is colored green, for Top 100 in yellow, Top 1000 red, otherwise violet."
What if I find out what is the supposedly "non-fake" distribution of green/yellow/red/violet words, and generate my text accordingly?
Generally, whenever your detection strategy is "a spam generator would never do X", I simply update my spam generator to do X. (Note that "X" must be something that is relatively easy to calculate, not things like "this actually makes sense", because it must be something your detector can recognize.)
Also, if Google suddenly started penalizing all text that doesn't "seem natural", there would be tons of false positives: languages other than English, jargon-heavy websites, dyslectic people, etc.
Loss rates of honey bee colonies during winter 2017/18 in 36 countries participating in the COLOSS survey, including effects of forage sources
Abstract:
This short article presents loss rates of honey bee colonies over winter 2017/18 from 36 countries, including 33 in Europe, from data collected using the standardized COLOSS questionnaire. The 25,363 beekeepers supplying data passing consistency checks in total wintered 544,879 colonies, and reported 26,379 (4.8%, 95% CI 4.7–5.0%) colonies with unsolvable queen problems, 54,525 (10.0%, 95% CI 9.8–10.2%) dead colonies after winter and another 8,220 colonies (1.5%, 95% CI 1.4–1.6%) lost through natural disaster. This gave an overall loss rate of 16.4% (95% CI 16.1–16.6%) of honey bee colonies during winter 2017/18, but this varied greatly from 2.0 to 32.8% between countries. The included map shows relative risks of winter loss at regional level. The analysis using the total data-set confirmed findings from earlier surveys that smaller beekeeping operations with at most 50 colonies suffer significantly higher losses than larger operations (p < .001). Beekeepers migrating their colonies had significantly lower losses than those not migrating (p < .001), a different finding from previous research. Evaluation of six different forage sources as potential risk factors for colony loss indicated that intensive foraging on any of five of these plant sources (Orchards, Oilseed Rape, Maize, Heather and Autumn Forage Crops) was associated with significantly higher winter losses. This finding requires further study and explanation. A table is included giving detailed results of loss rates and the impact of the tested forage sources for each country and overall.
1. Defining the Independence of the Liver Circadian Clock
Mammals rely on a network of circadian clocks to control daily systemic metabolism and physiology. The central pacemaker in the suprachiasmatic nucleus (SCN) is considered hierarchically dominant over peripheral clocks, whose degree of independence, or tissue-level autonomy, has never been ascertained in vivo. Using arrhythmic Bmal1-null mice, we generated animals with reconstituted circadian expression of BMAL1 exclusively in the liver (Liver-RE). High-throughput transcriptomics and metabolomics show that the liver has independent circadian functions specific for metabolic processes such as the NAD + salvage pathway and glycogen turnover. However, although BMAL1 occupies chromatin at most genomic targets in Liver-RE mice, circadian expression is restricted to ∼10% of normally rhythmic transcripts. Finally, rhythmic clock gene expression is lost in Liver-RE mice under constant darkness. Hence, full circadian function in the liver depends on signals emanating from other clocks, and light contributes to tissue-autonomous clock function.
2. BMAL1-Driven Tissue Clocks Respond Independently to Light to Maintain Homeostasis
Circadian rhythms control organismal physiology throughout the day. At the cellular level, clock regulation is established by a self-sustained Bmal1-dependent transcriptional oscillator network. However, it is still unclear how different tissues achieve a synchronized rhythmic physiology. That is, do they respond independently to environmental signals, or require interactions with each other to do so? We show that unexpectedly, light synchronizes the Bmal1-dependent circadian machinery in single tissues in the absence of Bmal1 in all other tissues. Strikingly, light-driven tissue autonomous clocks occur without rhythmic feeding behavior and are lost in constant darkness. Importantly, tissue-autonomous Bmal1 partially sustains homeostasis in otherwise arrhythmic and prematurely aging animals. Our results therefore support a two-branched model for the daily synchronization of tissues: an autonomous response branch, whereby light entrains circadian clocks without any commitment of other Bmal1-dependent clocks, and a memory branch using other Bmal1-dependent clocks to “remember” time in the absence of external cues.
