Original:  It consists of more than 800,000 seawater samples including measurements of nutriments (set aside in our experiments) together with pressure, temperature, salinity, water density, dynamic height (providing five input parameters), as well as dissolved oxygen, and oxygen saturation (the two outputs we would like to predict).
Revised: 6 It consists of more than 800,000 seawater samples including measurements of nutriments (set aside in our experiments) together with pressure, temperature, salinity, water density, dynamic height (providing five input parameters), as well as dissolved oxygen, and oxygen saturation (the two outputs we would like to predict).

Original: We assume that we can measure if any weighted sum of oxygen concentration and saturation is above a threshold by letting some population of bacteria evolves in the water sample and checking if it survives after a day.
Revised: We assume that we can measure if any weighted sum of oxygen concentration and saturation is above a threshold by letting some population of bacteria evolves in the water sample and checking if it survives after a day.

Original: If the measurements are done on the day of the sample collection, this setting exactly fits in the streaming active labeling framework.
Revised: If the measurements are done on the day of the sample collection, this setting exactly fits in the streaming active labeling framework.

Original: After cleaning the dataset for missing values, the dataset contains 655,140 samples.
Revised: After cleaning the dataset for missing values, the dataset contains 655,140 samples.

Original: The “CalCOFI” dataset results are reported on the left of Figure 11, parameters were chosen as 𝑝 = 100, 𝜎 = 10, 𝜆 = 10 − 6 and 𝛾 

Revised: The “CalCOFI” dataset results are reported on the left of Figure 11, parameters were chosen as 𝑝 = 100, 𝜎 = 10, 𝜆 = 10 − 6 and 𝛾

Original: =
Revised: =

Original: For the passive strategy, random queries were chosen to follow a normal distribution with the same mean as the targets and one third of their standard deviation ( i.e. we ask if the apparent temperature is lower than the usual one plus or minus a perturbation).
Revised: For the passive strategy, random queries were chosen to follow a normal distribution with the same mean as the targets and one third of their standard deviation ( i.e. we ask if the apparent temperature is lower than the usual one plus or minus a perturbation).

Original: The plotted baseline corresponds to linear regression performed over the entire dataset.
Revised: The plotted baseline corresponds to linear regression performed over the entire dataset.

Original: It takes about 10,000 samples for our active strategy to be competitive with this baseline, and 200,000 samples for the passive one.
Revised: It takes about 10,000 samples for our active strategy to be competitive with this baseline, and 200,000 samples for the passive one.

Original: The second experiment makes use of data collected through the Dark Sky API (which is now part of
Revised: The second experiment makes use of data collected through the Dark Sky API (which is now part of

Original: Apple WeatherKit).
Revised: Apple WeatherKit).

Original: It is made of 96,454 weather summaries between 2006 and 2016 in the city of
Revised: It is made of 96,454 weather summaries between 2006 and 2016 in the city of

Original: Szeged, Hungary.
Revised: Szeged, Hungary.

Original: Our task consists in computing the apparent temperature from real temperature, humidity, wind speed, wind bearing, visibility and pressure.
Revised: Our task consists in computing the apparent temperature from real temperature, humidity, wind speed, wind bearing, visibility and pressure.

Original: The apparent temperature is an index that searches to quantify the subjective feeling of heat that humans perceive, it is expressed on the same scale as real temperature.
Revised: The apparent temperature is an index that searches to quantify the subjective feeling of heat that humans perceive, it is expressed on the same scale as real temperature.

Original: One way to measure it would be to ask some humans if the outside is hotter or colder than a controlled room with a specific temperature and neutral meteorological conditions.
Revised: One way to measure it would be to ask some humans if the outside is hotter or colder than a controlled room with a specific temperature and neutral meteorological conditions.

Original: Once again, this exactly fits into our streaming active labeling setting.
Revised: Once again, this exactly fits into our streaming active labeling setting.

Original: The “Weather” dataset results are reported on the right of Figure 11.
Revised: The “Weather” dataset results are reported on the right of Figure 11.

Original: The baseline consists in predicting the apparent temperature as the real temperature.
Revised: The baseline consists in predicting the apparent temperature as the real temperature.

Original: We observe a transitory regime where the first 1,000 samples seem to be used to calibrate the weights 𝛼 .
Revised: We observe a transitory regime where the first 1,000 samples seem to be used to calibrate the weights 𝛼 .

Original: During this regime, our estimate is too bad for the active strategy to make smarter queries than the “random” ones that have been calibrated on temperature statistics.
Revised: During this regime, our estimate is too bad for the active strategy to make smarter queries than the “random” ones that have been calibrated on temperature statistics.

Original: The main difference in the learning dynamic between the active and passive strategies isobserved on the remaining 69,000 training samples.
Revised: The main difference in the learning dynamic between the active and passive strategies is

Original: The parameters were the same as the “CalCOFI” dataset but for 𝛾 0 = 10 − 2 .